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Multi-Objective Agent-based Modeling and Optimization of
Single Stream Recycling Programs
August 2013
PI: Nurcin Celik, Ph.D.
Assistant Professor
Department of Industrial Engineering
University of Miami, Coral Gables, FL, USA
Telephone: 1-305-284-2391, E-mail: [email protected]
Research Team
Xiaoran Shi, Aristotelis E. Thanos, and Eric D. Antmann
Hinkley Center for Solid and Hazardous Waste Management
University of Florida
4635 NW 53rd Avenue, Suite 205
Gainesville, FL 32653
www.hinkleycenter.org
Report # 1232031
1
TABLE OF CONTENTS
EXECUTIVE SUMMARY .......................................................................................................................... 2
RESEARCH TEAM ..................................................................................................................................... 4
DISSEMINATION ACTIVITIES ................................................................................................................ 5
1. INTRODUCTION ................................................................................................................................ 7
2. BACKGROUND AND LITERATURE REVIEW ............................................................................. 10
2.1 Previous Case Studies ................................................................................................................. 10
2.2 Related Works ............................................................................................................................. 13
3. PROPOSED METHODOLOGY ........................................................................................................ 14
3.1 Overview of the Proposed Approach .......................................................................................... 14
3.2 Structured Database .................................................................................................................... 16
3.3 Simulation Module ...................................................................................................................... 21
3.4 Fleet Utilities and Resource Allocation Optimization Module ................................................... 24
3.4.1. Objective Function .............................................................................................................. 24
3.4.2. Constraint Set ...................................................................................................................... 26
3.4.3. Solution Procedure .............................................................................................................. 27
4. EXPERIMENTS AND RESULTS ..................................................................................................... 31
4.1 Identification of the Bottleneck Facilities ................................................................................... 31
4.2 Comparison of Performances of the SSR and DSR Systems ...................................................... 34
4.3 Impacts of Participation Rate ...................................................................................................... 38
4.4 Fleet Utilization and Vehicle Routing Optimization .................................................................. 40
5. DISCUSSION AND CONCLUSIONS .............................................................................................. 41
6. REFERENCES ................................................................................................................................... 43
APPENDIX A: WASTE COMPOSITION BY COUNTY ........................................................................ 46
APPENDIX B: MSW COLLECTION SERVICE SURVEY IN STATE OF FLORIDA ......................... 48
APPENDIX C: SELECTED DOCUMENTS REVIEWED ...................................................................... 49
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EXECUTIVE SUMMARY
PROJECT TITLE: Multi-Objective Agent-based Modeling and Optimization of
Single Stream Recycling Programs
PRINCIPAL INVESTIGATOR: Nurcin Celik, Ph.D.
AFFILIATION: Department of Industrial Engineering, University of Miami
COMPLETION DATE: July 2013
OBJECTIVES:
In this research, our goal is to develop an agent-based simulation-based decision making and
optimization framework for the effective planning of single-stream recycling (SSR) programs. The
proposed framework will be comprised of two main modules: 1) the simulation module, and 2) the fleet
utilities and resource allocation optimization module. The simulation module is where various sources of
system uncertainties will be parameterized and incorporated into the simulation model of SSR.
Alternatives of SSR (i.e., dual-stream recycling (DSR)) with respect to characteristics, cost,
environmental impacts, bottleneck facilities, types and capacities of the processing facilities needed,
convenience for public participation factors will also be evaluated at this module. In the fleet utilities and
resources allocation optimization module, we will formulate the multi-criteria problem of allocation of
limited resources with a view to optimize routing and fleet utilizing aspects. Then, the optimum
combination of variables will be determined via the embedded genetic algorithm based optimization
mechanism for the state of Florida to reach its 75% recycling goal. Here, the optimum solution is
considered as the combination of variable which will lead to highest recycling percentage with minimum
cost and maximum benefits. Using the proposed tool, the stakeholders will be able to test several “what-
if” scenarios in their system before reaching to a conclusion.
METHODOLOGY:
The proposed framework includes the following components:
1. Database: A structured database was developed including all SWM facilities and generation units
located in the State of Florida. Facilities included landfills, waste-to-energy (WTE) facilities, material
recover facilities (MRF), and transfer stations (TS). Information regarding to generation units and
road distances among all the generation nodes are collected and analyzed in order to identify the
Voronoi cells. A total of 1.09 million of such cells were defined from the network of 1.75 million
road identified by the United State Census bureau. Generation units are classified as either single-
family residential, multi-family or commercial/industrial/institutional, and assigned to the intersection
in which they are located based on the developed Voronoi diagrams. Moreover, all data regarding to
the franchised haulers including their ownerships, locations, capabilities, service areas, collection fees,
3
etc., are collected and stored into the prepared database. All data in the database is in a human-
readable format for ease of modification or addition after the framework is delivered.
2. Simulation Module: The hybrid agent-discrete simulation model is expanded based on our earlier
work on Simulation-Based Optimization Framework for Solid Waste Management and Recycling
Program that was supported by the Hinkley Center. Upon the initialization, the simulation model
prompts users to select one or multiple counties to define a study region. Once a study region is
defined, the appropriate facilities and generation units (model agents) are loaded from the database.
Mechanisms used to generate and transfer wastes and recyclables in the simulation model are updated
significantly, with all generation, transfers, and facility loads characterized both by volume and
material composition, for simulating different types of collection systems (i.e., single stream, dual
stream, and multi-stream). In addition, a smart-linking mechanism and an integrated Geographic
Information System (GIS) are included in the simulation model, which creates meaningful and
quantified operational and economic links between cities and facilities and provides the road-network
distance between agents that have linkages, respectively.
3. Fleet utilities and resource allocation optimization module: The fleet utilities optimization module
utilizes an embedded genetic algorithm based optimization mechanism to simultaneously optimize the
decision variables defined in this study. The optimization mechanism references the existing system
for starting conditions, and maintains capacity feasibility at all times. The performance of each
candidate solution, in terms of overall and per-agent economic, environmental, and social constraints
and objectives, is evaluated in the model, and used to hone candidates in order to achieve the most
optimal routing, fleet utilization and resource allocation. Feedback from the real system is then used
to tune the proposed model, update the database and GIS system, and enhance the overall proposed
framework.
MAJOR RESEARCH FINDINGS:
Major findings of this research study may be summarized as the following:
− The single stream recycling system (SSR) is not suitable for all the counties in the State of
Florida. Its performance in terms of the total cost is highly related with the total waste recycled,
processed and disposed throughout the entire system. Therefore, in general, for large counties
such as Miami-Dade County, Broward County, Palm Beach County, etc., it is recommended to
utilize the SSR system; while for rural counties with a small population, such as St. Lucie,
Manatee, Volusia, amongst many others, the dual stream recycling system is recommended.
− Several major recycling facilities (i.e., material recovery facilities, waste-to-energy plants) are
overloaded in the system; thereby block the improvement on the recycling rate of the entire state.
In order to increase the recycling rate to reach the 75% goal, those bottleneck facilities have to be
enlarged, or new recycling facilities have to be constructed.
− With current residents’ participation rate of recycling, it is difficult to reach the 75% recycling
goal in the state. It is found that counties which have good education programs usually have
higher recycling rate. In addition, counties which do not provide the curbside collection services
to their residents usually have lower recycling rate. Therefore, in order to increase the recycling
rate, residents’ participation rate of recycling has to be increased through different outreach and
public programs.
− In order to achieve the economies of scale associated with large, centralized facilities, the
implementation of county or regional-level cooperative agreements, in which generation units
pool their waste streams, is also recommended.
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RESEARCH TEAM
Nurcin Celik (P.I.) is an assistant professor at the Department of Industrial
Engineering at the University of Miami. She received her M.S. and Ph.D. degrees in
Systems and Industrial Engineering from the University of Arizona. Her research
interests lie in the areas of integrated modeling and decision making for large-scale,
complex and dynamic systems. Her dissertation work is on architectural design and
application of dynamic data-driven adaptive simulations for distributed systems. She
has received several awards, including AFOSR Young Investigator Research Award
(2013), University of Miami Provost Award (2011), IAMOT Outstanding Research Project Award
(2011), IERC Best Ph.D. Scientific Poster Award (2009), and Diversity in Science and Engineering
Award from Women in Science and Engineering Program (2007). She can be reached at
Xiaoran Shi is a Ph.D. student in the Department of Industrial Engineering at the
University of Miami. Her research interests include sequential Monte Carlo methods,
and integrated system simulations with their application to distributed electricity
networks and solid waste management. She earned her bachelor's degree from
Department of Industrial Engineering at the Nankai University, China. She earned the
title of Excellent Student and Excellent Student Leader of Nankai University. She can
be reached at [email protected].
Aristotelis E. Thanos is a Ph.D. candidate in the Department of Industrial
Engineering at University of Miami (UM). His research interests include simulation
and optimization of large-scale systems, with a focus on distributed electrical
networks. He can be reached at [email protected].
Eric D. Antmann is an undergraduate student in the Department of Civil,
Architectural, and Environmental Engineering at the University of Miami. His
research interests include solid waste management, water resources, and the
simulation of large-scale systems. He has received several awards, including the John
Farina Memorial Endowed Scholarship, Florida Air and Waste Management
Association Scholarship, and first place at the University of Miami Research,
Creativity, and Innovation Forum. He can be reached at [email protected].
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DISSEMINATION ACTIVITIES
CONFERENCES AND INVITED TALKS:
1. Institute of Industrial Engineers (IIE) Southeast Region Conference; March 13,
2013, Miami, Florida.
2. Industrial and Systems Engineering Research Conference (ISERC); May 18-22,
2013, San Juan, Puerto Rico – The conference paper (entitled “Hybrid
Simulation-based Planning and Evaluation Framework for Solid Waste
Management and Recycling Systems”) was peer-reviewed and selected for
inclusion the Conference Proceedings.
3. Hinkley Advisory Board Meeting; May 13, 2013, Orlando, Florida.
SITE VISITS AND PRESENTATIONS:
1. The team presented to Waste Management, Inc. on September 11, 2012. Waste
Management, Inc. provided feedback on their needs, objectives, and concerns
from the proposed framework and discussed contributing background data on their operations and
suggestions for further case study development.
2. The team visited the Waste Management, Inc. at Pompano Beach, Broward County on March 19,
2013, and presented ongoing project work, preliminary results and recommendations obtained
throughout the study.
3. The team visited the Miami-Dade County Department of Public Works and Waste Management’s
Resource Recovery Facility (RRF), a waste-to-energy facility, on July
28, 2011.
4. The team presented project work to the University of Miami visiting
committee on Friday, April 19, 2013.
5. The team presented project work at the Florida Department of
Environmental Protection, Tallahassee on Wednesday, August 7, 2013.
TECHNICAL AWARENESS GROUP (TAG) MEETINGS:
The project team hosted three TAG meetings, on November 1, 2012, March 20, 2013, and August 15,
2013. List of our technical awareness group included:
Table 2: List of Confirmed TAG Members Name Position Affiliation Email
Helena Solo-
Gabriele
Professor University of Miami, Department of
Civil and Environmental Engineering
Kathleen Woods-
Richardson
Director Department of Public Works and Solid
Waste Management at the Miami-Dade
County
Paul J. Mauriello Deputy Director for
Waste Operations
Department of Public Works and Solid
Waste Management at the Miami-Dade
County
Shihab Asfour Professor University of Miami, Department of
Industrial Engineering
6
Brenda S. Clark Professional Engineer HDR Engineering Inc. [email protected]
Patti Hamilton Vice President and
Director of Business
Development
Southern Waste Systems and Sun
Recycling
phamilton@southernwaste
systems.com
Ram Narasimhan Professor University of Miami, Department of
Mechanical and Aerospace Engineering
Peter Foye Director Recycling and Contract Administration
Division at the Broward County
Kenneth P.
Capezzuto
Executive Director Environmental Health and Safety at the
University of Miami
du
WEBSITE: The team worked on and posted an enhanced website describing the project, accessible at
http://www.ie.miami.edu/celik/swmwebsite/default.htm
7
1. INTRODUCTION
Over the past two decades, the quantity of Municipal Solid Waste (MSW) produced by the United States
and particularly in the state of Florida has increased significantly (by over 73%), outpacing all other
nations and domestic population and economic growth. In response to this burgeoning waste stream, as
well as a multitude of other environmental, economic, and social factors, curbside recycling programs
have been established throughout the United States, with their number more than doubling in the decade
from 1990 to 2000 (Jamelske and Kipperberg, 2006). Yet, Florida’s current recycling rate of solid waste
hovers around 30%, a goal that was set by the Solid Waste Management Act of 1988 more than two
decades ago. In 2008’s legislative session, this statewide recycling goal has ambitiously been set to 75%
to be reached by 2020 (FDEP, 2010). To this end, extensive quantitative assessment of these possibilities
and coherent decision making on how to canalize already limited resources among existing alternatives
while maximizing total benefits becomes a necessary.
Recycling reduces the emissions of many greenhouse gases and water pollutants, saves energy,
supplies valuable raw materials to industry, creates jobs, generates less solid waste and conserves
resources while reducing the need for new landfills and combustors. However, planning for recycling is
quite challenging due to its large-scale, complex and dynamic nature evolving to address new economic
and environmental issues. As part of recycling, waste collection, which results in the passage of a waste
material from the generation nodes to either the point of treatment or final disposal, has changed
dramatically as collectors have introduced innovations and new collection efficiencies in various forms of
programs. The collector type (private or public), collection type (curbside collection, drop-off service,
on-call service), separation type (single stream recycling, dual stream recycling, multiple stream
recycling) are all factors that have significant impacts on the collection methods of waste management
programs. Furthermore, types of necessary equipment (open or closed roll-off bins, containers, etc.),
collection schedule and route, and some other social, political, economic influences on collection should
also be addressed at this point. The complexity of these factors necessitates the use of modeling and
optimization tolls for the design of effective, and economically viable, recycling programs. However, the
growth and development of these programs is always characterized by the conflicting objectives of
increasing both the quantity and quality of recyclable materials recovered (Biddle, 1998).
One emerging response to these potentially divergent objectives is the use of single-stream
recycling (SSR) program. It refers to a system in which all the recyclables are mixed together in a
collection truck, instead of being sorted into separate commodities by the resident and handled separately
throughout the collection process. In single stream system, both the collection and processing methods are
designed to handle the fully commingled mixture of recyclables, with material being separated for reuse
at a materials recovery facility. In contrast, dual-stream recycling (DSR), as the traditional curbside
8
recycling program, is a system in which generation units must separate materials prior to collection into
separate containers for paper, and metals and plastics. These separate containers are either collected by
different collection vehicles or by collection vehicles with multiple compartments, and are processed at a
material recovery facility (MRF) designed for dual-stream arrivals. Table 1 displays the detailed
comparison of these two recycling programs in terms of their advantages and disadvantages.
Table 1: Comparison between single-stream recycling and dual-stream recycling
Method Advantages Disadvantages
Single-
Stream
Recycling
Reduced sorting effort by residents,
higher participation rate in recycling
Automated collection and cheaper
single-compartment trucks lead to a
reduced collection cost
Greater fleet flexibility
Sorted at material recovery facility leads to
higher processing cost
Higher potential for contamination of paper
reduced the revenue from recycling
Decreased recycling rate
Dual-
Stream
Recycling
Higher recycling rate with less changes
of contamination
Lower processing cost and higher
working efficiencies in material
recovery facility
Great sorting efforts by residents are needed
Collection cost is higher because it may
needs multiple types of trucks and more
workers
Fleet management is much more complicated
Particularly, the SSR program leverages reduced effort requirements to increase the volume of
recyclables received, and improving material recovery facility (MRF) technologies to facilitate
economically viable post-collection sorting of recyclables. On one hand, single stream collection offers
reduced sorting effort by residents since all paper fibers, plastics, metals, and other containers are mixed
together in one collection truck, increased participation rate of recycling, improved ease and efficiency of
collection with mechanized lifting of carts, associated cost savings in collection, reduced injuries on the
job, and increased tonnage collected and recycling rate (see Figure 1 (L) for statistics), and diversion rate
from the local landfill (Kinsella and Gertman, 2007). On the other hand, since the SSR requires a more
sophisticated system, cost increases may come in single stream in the form of new carts/bins, new trucks,
education of residents, and the construction or renovation of materials recover facilities (MRF) or
recycling center (Annis and Walsh, 2007). Users of recycled materials also note wide variance in the
purity and quality of the output of SSR systems (Clapp, 2006), and increased supply of recyclable
materials has caused significant price instability in past years (Cooper, 1998). Despite these challenges,
the potential viability of the SSR system to improve on current recycling performance is clearly
manifested by the nature of municipal solid waste (MSW) stream. The composition of the MSW stream
from Broward County, Florida, representative of many mixed urban and suburban counties, is presented
in Figure 1 (R), depicting the presently disposed recyclable materials available. These statistics
9
corroborate that, if designed and operated in an economically and operationally sustainable manner, the
SSR system has the potential do dramatically increase overall recycling rates.
Figure 1: Changes in participation (in U.S.) and recycled tons (in Broward County, FL) for SSR
However, numerous challenges are present in the development and implementation of the SSR
system. Unlike SWM systems in general, in which collection and processing operations are in large part
independent, the SSR system is intimately dependent on the conditions of the road network. This system
also has elaborate fee structures and revenue structures, as fees vary between generation units and
geographic regions, and revenues vary based both on the quality and quantity of recycled materials
yielded. For these reasons, great disparities exist amongst implementations, in terms of economic
viability, materials addressed, level of service provided, environmental impact, and participation rate.
The high cost of sophisticated SSR-capable MRF’s exacerbates the necessity of accurate simulation tools,
as the development of an SSR system is a long-term commitment by both a community and a utility
provider.
To this end, we seek to remedy these challenges with a multi-objective agent-based modeling and
optimization framework for the single stream recycling system. Using the proposed decision making
framework, the operational and financial performances of the single stream recycling program may be
assessed to determine whether they are economically profitable considering the fact that a volume-based
collection system may increase the amount of illegal dumping and whether the increased contamination
result in poorer quality material and out-throws. These decision variables focus on the allocation of
limited resources amongst available infrastructure, including routing decisions and delivery allocations.
The framework consists of a structured database, a simulation module and a fleet utilities and resource
allocation optimization module. The database houses data regarding the location and parameters
(capacity, generation rate, processing fee, potential revenues, and greenhouse gas emissions) of
130,026
72481
8005 38371
10454
233041
31599
347025
18,
505476
28757 5417
26,
806577
0
10
20
30
40
50
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
MSW Recycled in Broward County, FL (tons)
MSW Collected & Recycled [Bubble size: collected waste in tons]
0%
10%
20%
30%
40%
50%
2002 2003 2004 2005 2006 2007 2008 2009 2010
Miami-Dade Broward CollierMartin Escambia
County Recycling Rate, FL (%)
[Red dots: the year in which the county changes to the SSR)
10
generation units, processing and treatment facilities, and franchised haulers. The road network connecting
these facilities is also included, in the form of TIGER/Line data provided by the United States Census
Bureau (USCB). By retaining all data in a human-readable format, this module allows for the easy
modification of the framework to different geographic regions or revised systems by the end user, thus
providing generality. The developed simulation module identifies and parameterizes system uncertainties
and properties, for inclusion in a holistic, agent-based model of the Integrated Solid Waste Management
System under review. At this point, alternatives of the SSR (i.e., the DSR), with respect to their
characteristics, cost, environmental impact, location, materials addressed, types and capacities of facilities
needed, government practices, collection frequencies, and convenience for public participation may be
also evaluated. The fleet utilities and resource allocation optimization module estimates route-level SSR
collection using an integrated Geographic Information System (GIS) featuring network and spatial
analysis algorithms and embedded genetic algorithm based optimization mechanism, in which the multi-
criteria problem of the allocation of limited resources are formulated and solved.
2. BACKGROUND AND LITERATURE REVIEW
The importance and the social and economic complexity of the problems related to environmental
protection in industrialized countries have continually increased during the last three decades (Schell and
Liu, 2009; and Huang et al. 2005). Accordingly, recent studies have predicted that the costs and benefits
of curbside recycling programs will vary considerably across municipalities (Aalbers and Vollebergh,
2008). The development and operation of the SSR program involves a variety of environmental,
economic, and social factors, all of which have a concerted interest in the performance of the program,
and have needs and constraints which must be considered in its design and implementation. Hence,
significant modifications to existing public waste management systems and behavioral norms may
become necessary in order to achieve the most optimal handling of recyclables in the MSW stream. In
order to achieve such an optimum, economic and operational analysis must be carried out from the
perspective of each distinct stakeholder. Such an analysis is challenged by the large, complex, and
multidisciplinary nature of MSW management and the SSR program, and has lead researchers and
utilities to search for more efficient methods. In this section, previous case studies related to the curbside
waste collection programs and researches on the simulation modeling and optimization methodology of
the SSR program will be provided.
2.1 Previous Case Studies
Regarding to the efficiency of providing residential solid waste collection service by different structural
arrangements (i.e., local governments provide services through their own employees local governments
11
contract for services from other governments and private agencies, etc.), series of surveys have been done
by Columbia University for the case of United States in the late of 1970s (Savas, 1977; Savas, 1978;
Savas, 1981). Reports for these surveys demonstrated that the healthy competitive climate carried out by
different agencies can help with improving the productivity and cost-effectiveness of solid waste
collection for citizens. As an extension of these studies, McDavid (1985) reports his findings from the
project that focuses on the experiences in contracting out collection services in Canada. Three different
structural arrangements, which are municipal collection, private collection, mixed collection, are
compared and reported in the sample of 126 cities in Canada. Results have unambiguously shown that the
collection done by franchised private companies (mixed collection) is substantially more efficient than
either the municipal or private collection. These studies have spurred the introduction of the mixed
collection structural arrangement and resulted in the current situation that both the public and private
agencies are responsible for the solid waste collection service, as of the case in state of Florida, which will
be our focus in this study.
Nowadays, more and more municipalities determine to transfer the collection system for
recyclables from the multiple or dual stream to a single stream, in which all recyclable materials including
cardboard, paper, glass, plastic, and metal are commingled. That is, residents throw everything into the
same container for pickup rather than sorting their recyclables before collection. Since single stream
recycling brings in more recyclables, increases the diversion rate, reduces the worker’s compensation
costs, reduces the number of trucks on the road, and often allows adding another material stream,
governments and contracted private haulers are satisfied with its economic benefits (Jamelske and
Kipperberg, 2006). However, recycled product manufacturers consider single stream as problematic with
the poor quality recyclables because of contamination and increased internal costs (Kinsella, 2006).
Therefore, the debating on the effectiveness and efficiency of residential SSR collection compared to the
multi-stream recycling (MSR) collection becomes more and more popular.
Several previous studies showed that single stream recycling programs improve the participation
rates of recycling amongst households, thus increase the total quantity of materials recycled compared to
the multi-stream recycling. In 1994, Gamba and Oskamp et al. conducted a case study in two adjacent
residential suburbs, Claremont and La Verne in Los Angeles, which present high similarity on
demographical, economical, historical and cultural characteristics. A commingled recycling program
(SSR program) is implemented in Claremont, whereas La Verne provides the separated recycling program
(multi-stream) during the periods when the case study is done. In this study, the authors compared the
performance of these two different kinds of recycling programs in terms of the participation rate of
recycling, the average quantity of materials recycled, contamination and long-term participation. Results
have shown that the recycling participation rate, even for the long-term participation rate is significantly
12
higher in the city with the SSR program than that with separated recycling program. In addition,
extremely difference is presented on the overall quantity of material recycled for these two programs,
with the SSR programs producing almost six times as much as the separated program, due to the greater
convenience of the comingled recycling procedure. However, the cities with the SSR program have to
bear the high start-up costs of providing residents with large recycling bins and the expense of sorting the
mixed recyclables in the material recovery facilities. In a similar vein, Wang (2006) examined three
jurisdictions, considering changes in recycling rates under a switch from multi-stream to a single-stream
program in the San Francisco Bay Area. Except for the results demonstrating the differences on the
participation rate, this study also shows that if there were differences in the magnitudes of the changes
through the three-case comparative design analyzed via multiple regression. Results carried out from this
study reveal that the increase of recycling rate generated by single stream recycling is significant, in both
the total tonnage and the per capita level, whereas the increase is not in the same magnitude in the three
cities under study due to other factors (i.e., the education program, the volume of the recycling bin, etc.).
Therefore, great room for improving the performance of SSR programs is illustrated; thereby necessitates
the further study and development on it.
Except for the studies that compared the SSR program and the MSR program, some researches
have noted the factors that have impacts on the policies of the recycling programs through case studies.
Hong and Adams (1993) worked on a case study of Portland, Oregon, in which they conducted an
economic analysis of household solid waste recycling under the impacts of price incentives and socio-
economic factors, with the purpose of determining the policy of recycling programs. A data set
representing 2300 households in Portland is incorporated to model and simulate the waste generation and
recycling behavior. As a result, they demonstrate that the payment differences generated by different
collection frequencies and the disposal fees have significant impacts on the recycling participation, while
the income of the residents does not appear to affect the residents’ behavior on the recycling program.
Matsumoto (2011) examines how the characteristics of municipalities are reflected in curbside recycling
policies through a case study in Japan. Their analysis reveals that municipalities implement recycling
programs that fit the demographic profiles of their residents. They also find that the time constraints of
households, which are the time that the residents spend on sorting the recyclables before collection, have
particular impacts on the recycling programs. In addition, they demonstrate that the gender division of
household labor does impact the recycling policies. For instance, if the wives’ working hour is greater
than that of husband, adopting the multi-stream recycling programs decreases the participation of
recycling to a great extent.
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2.2 Related Works
Rubenstein-Montano and Zandi (1999) points the lack of a decision model that can be used by the
municipal council for defining the financial and technical needs to provide a garbage collection service
covering the whole population. Models proposed in the literature for the management and planning of
solid waste collection include early economic cost-estimation models, mathematical models such as linear
programming (Everett and Modak, 1996), mixed integer linear programming (Diamadopoulos et al.,
1995), dynamic programming, and fuzzy logics (Chang and Wang, 1997), and heuristic algorithm based
optimization models (Poser and Awad, 2006; Karadimas, et al., 2007; Hemmelmayr, et al., 2011)
concerning for the optimal routing and scheduling, the minimized cost as well as maximized benefits in
terms of environment, and material and energy recovery.
Biddle (1998) relies exclusively on simple, deterministic economic forecasting to project
performance, and focuses on cost reduction to advocate SSR programs as recycling solutions for diverse
geographic regions. Challenges of routing and collection of SSR materials are averted by utilizing
existing collection plans from MSW systems, and ramifications on the volume and purity of recovered
goods are disregarded. This economic-centric perspective is shared by the majority of the models
proposed in the literature review, which apply fundamentally economic techniques to address other
aspects of such systems to varying degrees. Jamelske and Kipperberg (2006) continue with more
advanced economic models, conducting a contingent valuation study of SSR programs from the
residential perspective, and using the results to carry out an elementary cost-benefit analysis. Although
the contingent valuation method offers insight on public support of SSR programs and potential fee
structures, it does not single-handedly capture the complex needs and objectives of all stakeholders to
SSR collection, and its role in integrated SWM systems. Brodin and Anderson (2008) also provide a
more advanced economic model, focusing on consumer value and value-added services to model
residential participation in SSR programs based on the characteristics of the service and cost. However,
by limiting analysis to residential waste streams, these studies exclude large swaths of the total MSW
stream, including recyclables, to be handled, and thus lack the completeness necessary for sound decision
support. Janz et al. (2011) uses deterministic economic models for an SSR case study in a large East
German city. This model assumes public participation to be complete and static, and lacks any rigorous
modeling of changes to collection or consideration of the quality of final output. Their proposed solution
goes so far as to assume that the total volume of collection containers can be decreased following the
adoption of an SSR program, and does not capture numerous parameters of facilities and waste streams,
or their interaction.
The aforementioned works from the literature prove appropriate only for well-structured
problems where many of these models do not consider the collection process in line with the disposal
14
process. Most of these models were also deterministic programming models where the system
uncertainties are neglected to a great degree or stochastic optimization models with hypothetical cases
(Chi, 2002). In order to solve solid waste collection problems in a realistic setting where the results are
practically useful, uncertainties within these systems must be taken into account via both quantitative and
qualitative factors. Antmann et al. (2012) notes the necessity of advanced simulation techniques for the
optimization of large-scale integrated SWM systems. This study invokes an agent-based model of the
system under review, and integrates a Geographic Information System (GIS) to provide accurate
georelational data, and multi-variable simulation-based optimization using meta-heuristics. As SSR
programs are of comparable scale and complexity to integrated SWM systems, these tools provide a level
of detail absent in SSR studies found in the literature review. Hence, this study proposes to build upon
existing literature on SSR programs by utilizing these advanced simulation-based optimization techniques
to optimize SSR systems. The proposed solution will progress upon earlier works in terms of breadth,
inclusivity, handling of uncertainty, and acceptance of dynamic inputs.
3. PROPOSED METHODOLOGY
The proposed agent-based topology allows for the simultaneous modeling of complex economic and
operation considerations, including revenues and costs, facility locations and capacity constraints, and the
characteristics of the recovered SSR stream, singlehandedly. Combining this advanced simulation model
with simulation-based optimization allows all of these factors to be considered singlehandedly, rather than
the economic modeling and optimization strategies employed by earlier solutions. The use of an
integrated GIS provides the proposed simulation-based optimization tool with the actual geographic
characteristics of collection routes, facility locations, and transfer operations, allowing for significantly
more accurate calculation and optimization of transportation-related costs, time frames, and fleet
requirements. The GIS is integrated with an embedded database system as well, allowing the proposed
framework to be adapted to any real or proposed SSR system under review simply by the inclusion of
readily-available geographic data (from the US Census Bureau) and facility inventory information.
Hence, the proposed solution provides a level of breadth, detail, inclusiveness, accuracy, and adaptability
greater than those found during the literature.
3.1 Overview of the Proposed Approach
Single-stream recycling is a system in which all recyclable materials such as fiber (newspaper, mixed
paper, catalogs, magazines and junk mail) and containers (glass, steel, aluminum and plastic) are placed
in one recycling bin in an unsorted way and sorted by state-of-the-art processing equipment at a regional
recycling center. The SSR provides operational convenience and reduced unit costs for the waste
15
generators (i.e. time per stop, trucks on street, and processing costs per ton) and increases diversion
through improved participation. Government or the collection agencies receive a part of their profits on
selling recycled materials and for re-processing. The SSR also offers a greater employee safety, and may
improve the city appearance since there is no battered, over full or multi-colored trash cans on the streets.
While the SSR offers quite a number of benefits for waste generators, collection companies, and the
government when compared to source separated collection; it also comes with some significant
challenges related to effective fleet management and processing. As such, the SSR program almost
exclusively acts as one component of Integrated Solid Waste Management and Recycling Systems. These
systems include the collection, processing, and disposal or sale of a wide variety of waste materials.
Integrated SWM systems typically serve as a single provider for all waste management activities serving
a given geographic area, and may be operated by a public utility, a single private utility, or several private
firms. In this study, we develop a simulation-based decision optimization framework for the SSR program
acting as a component of Integrated SWM systems, with an objective to maximize the mass of material
recycled while minimizing costs.
The proposed framework is composed of a structured database and two main modules: the
simulation module and the fleet utilities and resource allocation optimization module. The database
stores and filters noise out of the historical data necessary to initialize the framework. Data comes from a
variety of credible sources, including the United States Census Bureau, the Florida Department of
Environmental Protection, and current studies sponsored by the Hinkley Center for Solid and Hazardous
Waste Management. In particular, the database contains information on the inventory of SWM facilities
and the franchised haulers needed for the SSR program, including their ownership, operational
constraints, and economic parameters. It also stores information covering the points to be serviced by the
SSR collection. This information is provided by the United States Census Bureau, and covers both the
location of pickup sites (single-family residential curbside collection, multi-family residential and
commercial sites scheduled pickup) and the road network. All data in the database are stored in a human-
readable format, allowing easy addition or modification by end users. Information for the entire State of
Florida is housed in the database, allowing the geographic region under analysis to be selected by users.
The hybrid agent-discrete simulation model is developed for assessing alternative single stream
recycling plans, evaluating their effectiveness on a quantitative basis against a dual stream recycling plan,
identifying bottleneck facilities and testing “what-if” scenarios based on stakeholders’ demands. An
integrated Geographic Information System, featuring network and spatial analysis algorithms, is
incorporated in the simulation model and used to develop initial routing specifications based on the
imported georelational data. An optimization mechanism is also included in which the multi-criteria
problem of the allocation of limited resources is formulated and solved via a meta-heuristic optimization
16
system. The performance of the candidate solutions obtained via the optimization model is then
compared to capacity constraints and the objective of increasing recycling performance, and the meta-
heuristic algorithm proposes a new candidate system configuration to improve its performance. This
candidate is then run in the simulation model, and another performance analysis is carried out. Through
an iterative process, the optimum solution, considered as the combination of operating variables which
yields maximum percentage of recyclables recovered while minimizing the total cost, is determined. A
graphical overview of the proposed framework is presented in Figure 2.
Figure 2: Proposed simulation-based decision making framework for effective SSR programs
3.2 Structured Database
The development and operation of the proposed framework requires an extensive library of data, to be
stored in the structured database. Data regarding location and parameters (cost, capacity, generation rate,
and location) of the generation units, and processing and treatment facilities, is necessary for the accurate
representation of these in the hybrid agent-discrete model. Furthermore, detailed georelational
information regarding the road network is crucial to accurately model the transfer mechanisms utilized by
the real systems. Therefore, an in-depth data collection phase is carried out in this study.
Facility Inventory
Generation Units
Costs and Fee Structures
Road Network
Regulatory Environment
User Editable
Structured Database
SSR Characteristics
Costs
Location
Political and Public
Acceptability
Materials Addressed
Facility Inventory
Generation Units
Fleet Characteristics
Routing and
Scheduling
Alternatives to SSR
Data in
Human-Readable
Format
Simulation Module
Model and Algorithm Update
Proposed Multi-Criteria
Optimum
Parameterized
Data
Data in
Machine-Readable
Format
Initial Routing
Data Transfer
Real System Selective Database
Update and Fine-Tuning
Network
AnalysisSpatial Analysis
Agent-based Model
Multi-criteria Optimization Algorithm
Performance Analysis
Fleet Utilities Optimization Module
Geographic Information System
17
In the survey of processing and treatment facilities to be considered, both MRF’s used for SSR
and the remaining landfills and waste-to-energy facilities were included. This is because regardless of
source reduction and diversion programs, a non-recyclable component of the MSW stream will remain for
the foreseeable future. Therefore, the performance of disposal facilities for these materials, as well as the
impact of SSR on their operations and economic performance, remains of vital importance. The
parameters of these facilities, including their location, operating cost, capacity, hours, and greenhouse gas
emissions, as well as the geographic areas they serve at present, were obtained from a previous Hinkley-
funded project carried out by our research group, Simulation-Based Optimization Framework for Solid
Waste Management and Recycling Programs. This information was incorporated into this project
directly. During this process, the six regions that the FDEP divides the State of Florida into, by county,
were considered as well. Both the 302 facilities and six regions are presented graphically and their
distributions amongst the six regions are presented in Table 2. The wide variation amongst the regions, in
terms of their facility composition and generation rates, further corroborates the necessity of a
generalized, human-readable structured database. This database, combined with database-driven model
compiling, eliminates the need for hard-coding each distinct region, resulting in a single framework being
applicable to discrepant regions.
Table 2: Overview of Florida’s SWM regions and facility inventory
Facilities (Count/Total Daily Capacity in Tons)
LF MRF WTE TS
Southeast 12/57,475 7/11,520 29/26,858 21/21,645
South 5/4,080 1/ 636 8/3,465 6/1,540
Southwest 12/13,220 5/9,322 21/8,985 13/13,100
Central 10/14,470 1/ 350 18/7,210 15/7,230
Northeast 11/14,720 1/ 200 21/11,840 37/7,860
Northwest 11/5,982 2/3,500 21/5,714 13/3,140
Generation Units
(Count / Total Daily Generation in Tons)
Southeast 125/23,142
South 18/3,900
Southwest 78/18,535
Central 90/16,593
Northeast 88/9,127
Northwest 77/5,387
However, in addition to the 302 processing facilities, the State of Florida also has over 10 million
unique service locations. Due to the dependence of SSR on collection mechanisms, and thus the road
network, generation units are defined at each distinct service location. The inventory of generation units
18
is thus derived from the property tax parcel records, obtained from the Florida Department of Revenue.
All inhabited parcels in each county are incorporated into the structured database, and divided into single-
family residential, multi-family residential, and commercial, industrial, and institutional classes. Location
data is incorporated into the integrated GIS based on data obtained from the Florida Georelational
Database. In Figure 3 and 4, the generator composition, by class, is displayed from selected counties,
highlighting this variation. And in Figure 5, statewide data on the composition of collected and recycled
materials is presented. Appendix A presents the overall waste composition on a by-county basis,
highlighting these disparities. With this information, both the quantity and composition of each
generation unit’s waste stream can be determined.
Figure 3: Generation unit inventories of selected
counties (counted by number of units) Figure 4: Distribution of waste by selected counties
(per year)
Figure 5: Composition of collected and recycled materials (in tons)
0
100,000
200,000
300,000
400,000
500,000
Single Family Multi-Family Commercial
0
200,000
400,000
600,000
800,000
1,000,000
1,200,000
1,400,000
Single Family Multi-Family Commercial
0
1,000,000
2,000,000
3,000,000
4,000,000
5,000,000
6,000,000
7,000,000
8,000,000
C&D Alumiums
and Pastics
Metal Paper Food Waste Yard Trash Glass Process FuelCommercial
Collected Recycled
19
The parameters and location of the generation units and treatment facilities, while crucial to
modeling, is not sufficient to accurately model the real-world collection and transfer mechanisms, which
are highly dependent on the road network and routing. Therefore, in addition to these nodes, information
regarding the road network is needed. This data contains the location and length of the 840,000 primary,
secondary, and tertiary public roadways in the State of Florida. In the integrated GIS, this data is
combined with the generation and processing node locations to develop a complete network dataset.
Within this network dataset, network and spatial analysis algorithms can provide detailed and optimized
routing information. Thus, in Figure 6, all of the components necessary for collection and transfer
modeling are unified into a single georelational tool, at the neighborhood scale: source nodes, processing
nodes, and transfer arcs. The quantity, density, and proximity of these entities underscore both the
complexity of the SSR system under review and necessity of accurate georelational modeling.
Figure 6: Example neighborhood-level roadway and generation unit view
20
Beyond the georelational organization of Florida’s SWM infrastructure, operational and
economic parameters are also crucial to the proposed framework. Along these lines, a survey of all
haulers operating in the state was carried out (see Appendix B), seeking their service area, materials
addressed, and fees. This data, together with the processing and treatment facility cost and profit data
obtained from the earlier work by our research team, provides all cost and revenue sources in the SSR
system. A total of over 800 documents and websites were reviewed, 50 emails were sent out and 300
telephone calls were made in the process of this data collection. A small portion of the haulers’
information is presented in Table 3 below. A list of reviewed documents is presented in Appendix C.
Table 3: Detailed information of haulers by city
City Name County Hauler Public/
Private
Located
City Zip
Recycling
Stream
Service
Time
Collection
Fee
Stuart Martin
Sanitation and
Recycling
Department
Public Stuart 34994 Single 6:00-
18:00
$15.85
/month
Coral
Gables
Miami-
Dade
WM of Dade
County (Waste
Management Inc.)
Private Miami 33127 Single 7:00-
17:30
$21.20
/month
North
Miami
Miami-
Dade
Waste Pro.
Management Private Miami 33142 Single
7:00-
17:00
$26.00
/month
Boynton
Beach
Palm
Beach
City of Boynton
Beach Solid Waste
Division
Public Boynton
Beach 33435 Dual
6:00-
17:00
$14.00
/month
Greenacres Palm
Beach
Veolia
Environmental
Services
Private West Palm
Beach 33413 Dual
7:00-
18:00
$7.50
/month
Lauderhill Broward Republic Service
Inc. Private
Fort
Lauderdale 33311 Single
7:00-
19:00
$16.00
/month
Sebring Highlands
Choice
Environmental
Service
Private Sebring 33870 Single 6:30-
14:30
$12.75
/month
Plant City Hills-
borough
Plant City Sanitation
Divisions Public Plant City 33563 Dual
7:00-
16:00
$28.03
/month
Maitland Orange Waste Services of
Florida Inc. Private
Altamonte
Springs 32701 Dual
7:00-
17:00
$22.41
/month
Davenport Polk
Florida Refuse
(Republic Services
Inc.)
Private Lakeland 33801 Single 7:00-
17:00
$17.50
/month
Once this data is merged with the generation unit and processing facility data, it is then prepared
for the clustering process, in order to reduce the computational burden of the modeling and optimization
approach. Therefore, the combination of generation unit, processing and treatment facility, and road
network data, from operational, economic, and environmental perspectives, in the structured database
21
provides a unified dataset for the complete SSR life-cycle. By maintaining this information in a human-
readable format, generality is achieved, as users may easily modify data to reflect various perspective
SSR systems or different geographic regions. Thus, the thorough data collection phase provides the
resources necessary both to accurately simulate the SSR system or its alternatives and to provide an
adaptable and user-friendly resource to the end users. Figure 7 presents a screenshot of the structured
database incorporated after sub-clustering.
Figure 7: Screenshot of the structured database under sub-clustering
3.3 Simulation Module
In this study, the simulation module is updated based on the hybrid agent-discrete approach developed by
a previous Hinkley-funded project carried out by our research group, Simulation-Based Optimization
Framework for Solid Waste Management and Recycling Programs. This framework is fundamentally
expanded via the integration of generation units, the clustering and subclustering methods, and
identification mechanisms for bottleneck facilities. The modeling approach utilizes automated
constructors to provide for generality, by compiling the model on-demand based on coefficients defined
by database contents.
First of all, the development of the hybrid agent-discrete simulation model requires the
integration of numerous data sources and types into a single tool. These items include the inventory of
generation units (approx. 9 Million) and facilities, the haulers, and the road network (approx. 800,000
roads). In order to capture the many and diverse entities at play in the model, a clustering mechanism has
developed. The objective of this mechanism is to group generation units into clusters, for the assignment
of destination processing facilities for wastes and the selection of haulers, and sub-clusters, for the routing
22
of collection vehicles. Objectives of this mechanism were to reduce computational burden while
maintaining the greatest degree of accuracy and resolution possible. Therefore, postal ZIP codes were
utilized for the clusters, as these typically follow municipal or development patterns. In order to define
sub-clusters, a Voronoi diagrams based system is utilized, with street intersections forming the nodes of
the Voronoi cells. This method offers the advantage of scaling subcluster size and the density of the road
network, in order to provide greater granularity in more densely populated areas and lesser granularity
elsewhere. Figure 8 presents the screenshot of the Voronoi-based subclustering system.
Figure 8: Voronoi-based subclustering system
Then, mechanisms used to generate and transfer wastes and recyclables in the model are also
updated significantly, with all generation, transfers, and facility loads characterized both by volume and
material composition. This modification makes it possible to simulate different types of collection system
(i.e., single stream, dual stream and multi-stream) for both county-level experiments and state-level
experiments. In addition, the truck’s utilization and the processing cost in the material recovery facilities
(MRFs) are changed based on the collection system under consideration. Facilities provided for adjusting
gross generation and material composition by county and generation unit type. These additions allow the
model to more accurately capture both the volume and characteristics of recyclable materials to be
collected and processed by SSR or DSR systems, to aid in facility design and operation.
23
Last but not least, the animation of the previous model is significantly updated, with a better
analysis and presentation for the statistics obtained during the model running. Figure 9 provides a
screenshot of the expanded simulation model. Figure 10 presents the identification mechanism for the
bottleneck facilities.
Figure 9: Screenshot of the expanded simulation model
Figure 10: Bottleneck facility identification
24
3.4 Fleet Utilities and Resource Allocation Optimization Module
The parameters and uncertainties identified in the simulation module are then incorporated into the fleet
utilities and resource allocation optimization module. This module includes the inventory of generation
units, which are considered as the collection nodes, including their location and typical waste generation
in terms of daily waste generation rate and waste composition, the information of the haulers that provide
services in the particular area (i.e., owner, collection fee, recycling policy, etc.), and the inventory of
processing facilities, in terms of their types, locations and capacities. The road network and traffic
patterns are also included in this model. From this model, the GIS uses spatial and network analysis
algorithms combined with the genetic algorithm to calculate geographically optimal routing with the
specific objectives and constraints under consideration. Geographically optimal routing minimizes the
total distances of the truck traveled in order to collect all of the SSR materials. Once the near optimal
routing and resource allocation information is obtained, it sends feedback to the incorporated simulation
module, providing initial routing conditions.
3.4.1. Objective Function
In this study, three objectives are considered for optimization of the fleet utilizing and resource allocation
of the SSR system: 1) minimization of total driving distance during vehicle routing; 2) maximization of
global benefits for all the private/public agencies; 3) maximization of recycling rate. In order to reconcile
potentially contradictory objectives, a system of multiple aspiration levels is implemented. To this end,
the fuzzy multi-objective integer programming model is built by introducing the aspiration
levels , , , , , as the membership values and weights corresponding to three objectives
defined as following.
(a) Minimization of total driving distance during vehicle routing. For each clustered service area,
the hauler provides the collection service utilizing a fleet of collection vehicles, resulting in a vehicle
routing problem (VRP). This objective seeks to determine a set of delivery routes for the trucks starting
from the origin (the hauler’s service center) to the generation nodes, picking up and transferring wastes to
the treatment or disposal facilities, and then returning to the origin, to minimize the total distance covered
by the entire fleet. Since the capacity information of the haulers (the number of trucks in use) has been
collected and stored in the structured database, the optimization of the fleet utilities can be treated as the
minimization of total driving distances. Suppose in county , there are haulers that provide the waste
collection service for service areas. In each service area, there are generation nodes, and each
hauler owns trucks, where and present the th generation node and th hauler, respectively.
Meanwhile, there are also recycling facilities (MRFs) and disposal facilities (WTEs or Landfills).
Binary variables denote whether the th hauler provides service for th service area: a value of one
25
indicates this is true, while otherwise the th hauler doesn’t provide service for area . Furthermore,
, , , , , and are the distances between the hauler’s service center and generation
node, distances among generation nodes, distances between generation nodes and recycling and disposal
facilities, distances between recycling and disposal facilities and the hauler’s service center. In addition,
we introduce another set of binary variables , , , , , and to control the selection of
the truck’s routes. If the arc ( , ) etc. exist, the value of the corresponding binary variable (i.e., )
equals to one; otherwise, it is set as zero. Given all these notations, the objective function can be
provided as (1).
∑ ∑ ∑ ∑ ∑
(1)
(b) Maximization of global benefits for all the private/public agencies. The numerous contractors
participating in SWM systems often have conflicting objectives. In order to reconcile these, the
objectives of each stakeholder will be weighted by the user based upon the tonnage of the SSR processed.
Benefits considered are the balances of the total service revenue (collection fee paid by the residents)
extracting the total collection costs (transportation cost). For an th hauler, supposing it charges as a
collection fee to the households serviced, has fixed labor cost for collection is , and variable
transportation cost is per mile, the detailed objective function can be given as (2),
∑ (2)
where ∑ ∑ ∑ ∑
represents
the total distances that the trucks owned by the hauler travelled for.
(c) Maximization of recycling rate. In this study, the recycling rate is defined as the ratio of the
amount of waste transferred to MRFs divided by the amount of waste transferred to the disposal and
recycling facilities (WTEs, Landfills and MRFs) for a given area. For hauler which owns trucks, the
variables , represent the amount of wastes transferred from generation node to recycling
facilities , and wastes transferred from generation node to disposal facilities in area . Then the
objective related to the recycling rate can be presented as (3).
∑ ∑ ∑ ∑
∑ ∑ ∑ ∑
∑ ∑ ∑ ∑
(3)
As a result, the objective function with the min-max operator becomes a function in terms of both
the aspiration level ( ) and weights ( ) associated with three objectives defined in (1)-(3), which is
given in (4).
(4)
26
3.4.2. Constraint Set
The basic constraint consists of the objective constraints which are formulated by fuzzy mechanism, the
node selection condition, capacity limitation, and sub-tour breaking. Since the constraints will be not
treated as fuzzy ones, specific penalty functions are incorporated in the solution procedure (GA based
optimization procedure) to minimize the violation of these constraints. Formulations of the constraint set
are illustrated as follows:
(a) Objective constraints. As mentioned above, the multi-objectives in this study are modeled and
computed in a fuzzy mechanism, presented as constrains in (5)-(8), which are considered as part of fuzzy
version formulation in GA-based solution procedure,
(5)
(6)
(7)
where , , are the upper bound of tolerance interval and , , are the lower bound of
tolerance interval in each fuzzy membership function, respectively.
(b) Node selection condition constraints. These constraints ensure that each generation node
should be visited by at least one truck on every scheduled waste pick-up day. This enforces the provision
of adequate collection services to all generation units. In order to simplify the presentation, we assume
that the th hauler provides service for area ( ), which has a total number of generation
nodes. Meanwhile, binary variables represent the selection of the route corresponding to the
generation node and in the th service tour serviced by th truck. Then, these constraints for node
selection can be given in (8) and (9),
∑ ∑ ∑
(8)
∑ ∑ ∑
(9)
in which is the maximum number of service tours that a truck can make within a day, and are the
sets of all neighboring nodes around node and in the network, and and are the total number
of neighboring nodes for node and , respectively. These conditions can guarantee that the opportunity
for the all the trucks entering and leaving each node in the network is the same.
(c) Capacity limitation. Due to the limited capacity of haulers, determined by the number and
type of vehicles in service, this constraint ensures that the total capacity each hauler’s collection fleet can
satisfy the collection demands assigned by the optimization mechanism, under the candidate route plan, as
presented in (10), in which is the capacity of truck .
∑ ∑ ∑ ∑
∑ (10)
27
(d) Constraints for sub-tour breaking. These constraints are given in (11), aiming at eliminating
all the sub-tours in the optimization process, where denotes the set of all neighboring nodes.
∑ ∑
(11)
3.4.3. Solution Procedure
The optimization mechanism is based on the genetic algorithm (GA), and seeks to solve the vehicle
routing problem (VRP) created by curbside trash and recycling programs in order to optimize overall
performances. However, the VRP created by such systems differs fundamentally from traditional VRP
cases, as the vehicles have to search for a suitable disposal facility to unload the wastes before they go
back to the depot, or another collection route. Spatial and network analysis algorithms within the GIS are
utilized to calculate travel distances and provide the geographically optimum routing for any given trip.
These algorithms interface with the optimization mechanism, which alters linkages between generation
clusters, haulers, and processing facilities in order to achieve an optimum under the objectives and
constraints under consideration. Figure 11 shows a feasible solution to the extended VRP problem, in
which, 0 denotes the depot, 1-15 represent the customers (waste generation nodes), A, B are material
recovery facilities, C is a WTE, D and E are two landfills, respectively.
Figure 11: A feasible solution to an extended vehicle routing problem
The GAs were first introduced by John Holland (1975), which are used to model the natural
evolution by using genetic inheritance based on the idea behind Darwin’s theory. In the genetic
algorithm, a population of a set of solutions or individuals is maintained by the means of crossover and
1
2
34
5
6
7
8
9
10
11
12
13
14
15
A
B
C
D
E
0
Garbage collection and transfer flow Recyclables collection and transfer flow
28
mutation operators, where crossover simulates reproduction and mutation operators play a role of
generate random changes in the solutions. Each solution is assigned to a fitness value, working as a
selection procedure to ensure the high quality of the population. As the iteration increases, the quality of
the population gradually improves as new and better solutions are generated and worse solutions are
removed.
(a) Encoding. The genes of a chromosome in the encoding of the proposed optimization
mechanism correspond to the decision variables of the selection of haulers, potential routes, and the
allocation of the collected wastes to the processing facilities. In particular, each chromosome is
represented by an array, in which the numerical value of each gene is selected
from 0 to 1. In each row of the array, the first bytes represent the decision of hauler selection
(selecting=1/not selecting=0), the following bytes represent the visiting of the generation node
(visiting=1/not visiting=0), and the last bytes are the allocation decision meaning that the collected
wastes will be sent to the corresponding processing facilities. ( denotes to the number of
the row for the chromosome, and each row represents one step of the truck’s movement. To this end, the
genes of the first one byte should be the decision of the hauler selection, and the genes after the first one
byte in the first and last row should be 0, meaning that the truck starts from the hauler’s collection center
and goes back to the center once it finishes one service tour. Meanwhile, the last bytes of genes
before the row should be zero, satisfying the constraints that the trucks should finish visiting all
the generation nodes before sending wastes to the recycling or disposal facilities.
(b) Initializing the population and selecting the parents. Initializing the population in the GA
provides an initial gene pool for population generation, in which it is desirable to have each gene occur
with approximately the same frequency, in order to avoid bias issues. In this study, we decide to generate
the initial population randomly with a 0-1 sequence in each row. Detailed information of the initial gene
pool is provided in the Figure 12. The parents are selected randomly from the population.
Regarding to the constraint sets, the gene pool proposed here should be subjected to the following
limitations, as shown in (12)-(15).
∑ (12)
∑ ∑
(13)
∑ (14)
∑ ∑
(15)
29
Figure 12: Initial gene pool for the population initialization
(c) New member generation, mutation and fitness. For this problem, new members are generated
based on a swap rule. After the parents are selected, two rows (except for the first row and last three
rows) in the gene pool will be swapped to generate a new member. The performance of the generated
member will be judged by the fitness function, which is the objective value that the solution corresponds
to. If it provides better performance than its parents, it will be selected as the one of the new parents to
provide next generation; otherwise, another round of swapping will be carried out. Meanwhile, since the
solutions have been generated subjected to all the constraints, the fitness value and the encoding can
satisfy all the constraint sets and no additional efforts are needed for the implementation of the algorithm.
In order to help the algorithm keeping from the local optima, a mutation rate has been selected, which is,
in 3% of new member generation iterations, change a private hauler that was not found in the union of the
two parents before starting a new iteration.
(d) Termination. After searching for the solutions with different iterations, we adopt the
convergence-based stopping criterion in this study for the termination of the algorithm to export the near
optimal feasible solutions. Particularly, the algorithm terminates after observing √
successful iterations while the best solution found has not changed any more. Here, one iteration
represents triggering the generation operator once.
In our proposed optimization module, the algorithm starts with a clustering algorithm based on
the zip code area, in which one hauler is assigned to provide the waste collection service. Secondly, for
the purpose of reducing the computation burden, the developed sub-clustering algorithm is applied to
each cluster, to satisfy the conditions that each sub-cluster is served by one truck in one trip and the total
𝑋 𝑋 𝑋 𝑛 𝑋 𝑝 𝑋 𝑝 𝑋 𝑞 𝑋 𝑚 𝑋 𝑚 𝑋 𝑤
𝑋 𝑋 𝑋 𝑛 𝑋 𝑝 𝑋 𝑝 𝑋 𝑞 𝑋 𝑚 𝑋 𝑚 𝑋 𝑤
⋮𝑋 𝑛 𝑋 𝑛 𝑋 𝑛 𝑛 𝑋 𝑛 𝑝 𝑋 𝑛 𝑝 𝑋 𝑛 𝑞 𝑋 𝑛 𝑚 𝑋 𝑛 𝑚 𝑋 𝑛 𝑤
⋮𝑋 𝑞 𝑋 𝑞 𝑋 𝑞 𝑛 𝑋 𝑞 𝑝 𝑋 𝑞 𝑝 𝑋 𝑞 𝑞 𝑋 𝑞 𝑚 𝑋 𝑞 𝑚 𝑋 𝑞 𝑤
⋮𝑋𝐴 𝑋𝐴 𝑋𝐴𝑛 𝑋𝐴𝑝 𝑋𝐴 𝑝 𝑋𝐴𝑞 𝑋𝐴𝑚 𝑋𝐴 𝑚 𝑋𝐴𝑤
=
0 0 0 0 0 0 0 00 0 0 0 0 0 0
⋮0 0 0 0 0 0 00 0 0 0 0 0 0
⋮0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
30
waste generation amount in each sub-cluster is less than or equal to the truck’s capacity. Then, the hauler
is selected by the k-nearest neighbor algorithm. Finally, in order to solve the vehicle routing problem and
obtain the near optimal solution without the expense of enormous computation efforts, a genetic
algorithm (GA) based optimization mechanism is proposed in this study. In GA, a population of a set of
solutions or individuals is maintained by the means of crossover and mutation operators, where crossover
simulates reproduction and mutation operators play a role of generate random changes in the solutions.
Each solution is assigned to a fitness value, working as a selection procedure to ensure the high quality of
the population. As the iteration increases, the quality of the population gradually improves as better
solutions are generated and worse solutions are removed. Figure 13 shows the solution procedure of GA
for solving the fuzzy multi-objectives mixed integer-nonlinear programming problem in this study.
Figure 13: Solution procedure of fuzzy genetic algorithm
MODELING
Model formulation (define variables,
objectives and constraints)
Define the aspiration level and
weights for membership function
GA
Determine Selection Operator
Define Crossover Rate
Define Mutation Rate
Encoding
Generating Solutions
Select Fitness Value
Crossover
Mutate
Decoding
Fitness Evaluation: if
the critical condition or
stop rule is satisfied?
END
Yes
No
Adjust crossover rate
and mutation rate
31
4. EXPERIMENTS AND RESULTS
In order to demonstrate the feasibility and validity of the proposed model and provide recommendations
to governments or other stakeholders, several county-level experiments are carried out in this study.
4.1 Identification of the Bottleneck Facilities
In the first set of experiments, bottleneck facilities under the single stream recycling systems are
identified. A facility is defined as a bottleneck when the amount of waste transferred there exceeds its
capacity for at least one day of the month. It is noted that by increasing the capacities of the bottleneck
facilities, the corresponding recycling rate of is found to be improved. However, it stops increasing when
the bottleneck facility becomes a non-bottleneck facility with enough capability to deal with the wastes
that are sent there daily. It should be also noted here that the analysis on identification of bottleneck
facilities may also be detailed with inclusion of buffer times that helps store the waste that is currently
collected for processing at later times. In the results shown below, the buffer times are not considered.
Case I: Leon County
Leon County belongs to the northwest region in the State of Florida, in which there is only one
incorporated municipalities (i.e., City of Tallahassee) and 21 unincorporated communities. In its current
form, the Leon County SWM system is serving serve a population of 275,487 with a dual stream
recycling program through its five transfer stations and two material recovery facilities. Moreover, it has
to be highlighted that Leon County sends its wastes to be disposed in a landfill in Jackson County, which
is considered as the inter-dependent county for Leon County. Table 4 provides an overview of the Leon
County, and the details of the generation units, franchised haulers, and facilities.
Table 4: An overview of Leon County Solid Waste Management System
Facility Type
Daily
Capacity
(tons/day)
Leon County Transfer Station TS 1040
Woodville Rural Waste Collection
Center TS 90
Fort Braden Rural Waste Collection
Center TS 90
Miccosukee Rural Waste Collection
Center TS 90
Blount Rural Waste Collection Center TS 90
Generation Units
(tons/day) 1,012 Marpan Recycling MRF 170
32
Franchised Haulers Waste Management Inc. Regional Landfill (in Jackson
County) LF 3000
Throughout the experiments, the Marpan Recycling (MR), whose current capacity is 170 tons/day,
is found to be the bottleneck facilities in the system. Finally, by increasing its capacities with 20 tons in
each trial, the recycling rate is improved from 32.75% to 38.15%, and this improvement stops when its
capacity reaches to 260 tons/day. Table 5 provides the detailed results obtained for these experiments.
Table 5: Results for bottleneck facility identification and expansion in Leon County
Capacity of MR (tons/day) Total Waste Processed (tons) Recycling Rate (%)
170 403,976.40 32.75%
190 415,050.20 35.39%
210 421,576.80 37.14%
230 424,862.40 37.72%
250 425,785.40 38.09%
260 424,248.00 38.13%
Case II: Miami-Dade County
In the second case, Miami Dade County is selected as a case. Miami-Dade County SWM is
markedly more complex than that of other counties. Particularly, it serves a population of 2,496,435, and
handles over 1.3 million tons of waste each year, with three transfer stations, one material recovery
facility, one waste-to-energy plant, and four landfills. Detailed information regarding to the generation
units, facilities and haulers in the Dade County are provided in Table 6.
Table 6: An overview of Miami-Dade County Solid Waste Management System
Facility Type
Daily
Capacity
(tons/day)
South Dade Landfill LF 1800
North Dade landfill LF 5300
Medley Landfill Facility LF 6350
Ashfill LF 420
Miami-Dade County Resource
Recovery Facility WTE 3200
Generation Units
(tons/day) 10,670 Central Transfer Station TS 360
Franchised
Haulers
Waste Management Inc. West Transfer Station TS 650
Choice Environmental
Service Northeast Transfer Station TS 520
33
World Waste Service Inc. Waste Services of Florida-Miami MRF 3880
Throughout the experiments, the Central Transfer Station (CTS), with a 360 tons daily capacity,
is identified to be a bottleneck facility. Its capacity is also increased slowly via various replication of
simulations similar to that of Leon County. The experiments have shown that by enlarging the facility to
50% of its current capacity, the facility becomes a non-bottleneck facility. Consequently, the recycling
rate increased 1.8% for Miami-Dade County. The impact of improving the capacity of that facility to
50% is ingsignifcant on the recycling rate of Dade County. This is mainly due to the fact that the
bottleneck facility is a transfer station whose impact on recycling rate in the entire SWM system is not
significant. Table 7 provides the detailed results obtained for these experiments.
Table 7: Results for bottleneck facility identification and expansion in Miami-Dade County
Capacity of CTS (tons/day) Total Waste Processed (tons) Recycling Rate (%)
360 3,444,436.80 35.85
396 3,575,152.80 35.96
432 3,709,382.40 36.12
468 3,835,342.40 36.58
504 3,893,083.20 37.02
540 3,894,342.40 37.65 576 3,894,106.30 37.63
From these two cases, it can be noted that the proposed hybrid simulation module is able to
identify the bottleneck facilities in the SWM system. And once the facility is demonstrated as a
bottleneck throughout the entire system, increasing its capacity is highly recommended. However,
increase in recycling rate is not linear with the increase in the bottleneck facility’s capacity (see Figure 14
for details). Therefore, a balance point at which the ratio of the recycling rate to the capacity stops
increasing or approaches to zero has to be found, to maximize the stakeholders’ benefits. It should also be
noted here that in order to determine the best combination of capacities of facilities a design of
experiments study should be conducted. This way, increasing the capacities of multiple facilities
simultaneously, or elimination of some capacities while enlarging some others may be studied in detail.
34
Figure 14: Analysis of recycling rate versus bottleneck facility’s capacity in selected counties
Experiments for identification of the bottleneck facilities have been conducted for all the 67
counties in this study and results have shown that increasing the capacities of the bottleneck facilities may
improve the recycling rate significantly. Bottleneck facilities in the top 10 counties in terms of their
populations are listed in the Table 8 as the following.
Table 8: Lists of bottleneck facilities in selected counties
County Facility Name Type Current Capacity (tons/day)
Miami-Dade Central Transfer Station TS 360
Broward None - -
Palm Beach West Central Transfer Station TS 1000
Hillsborough Metro Recycling - Tampa MRF 700
Orange Recycling America of Orange MRF 700
Pinellas Waste MGMT Pinellas MRF MRF 700
Duval Southland Recycling Services MRF 1100
Lee Lee County Resource Recovery Facility WTE 636
Polk Ridge Energy Generating Station WTE 822
Brevard Melbourne Landfill (Florida Recyclers of Brevard) MRF 950
4.2 Comparison of Performances of the SSR and DSR Systems
In the second set of experiments, all 67 counties are selected, running with 10 replications for both single
stream recycling system (SSR) and dual stream recycling system (DSR), for 1 year operation (2012-
2013), given the assumption that residents’ participation rates for recycling are the same under both of
these two systems. As has been highlighted earlier in the database module of our study, the accuracy of
the decision support tool developed in this study is heavily dependent upon the accuracy of information
32
33
34
35
36
37
38
39
170 190 210 230 250 260
Rec
ycl
ing
Ra
te (
%)
Capacity (tons)
Leon County
Recommended
Capacity
34
35
36
37
38
360 396 432 468 504 540 576
Rec
ycl
ing
Ra
te (
%)
Capacity (tons)
Miami-Dade County
Recommended
Capacity
35
provided and contained in the database. The current what-if analyses conducted based on the latest
information that the team was able to collect from different counties in the State of Florida.
In the considered model, while under the SSR system, only one type of pick-up truck is utilized
for the collection of the recyclables assuming that the extra sorting process is occurred in the MRFs with a
cost of $7/ton. This extra sorting process cost is estimated by Chester et al. (2008) as national average
cost for entire United States, when considering the sorting at MRFs under SSR systems. In contrast, two
types of pick-up trucks are utilized in the DSR system for the collection of the recyclables, and paper,
respectively. Furthermore, capital cost for the existed facilities is included by the way of transferring the
long-term fixed cost to a tonnage-based unit cost, considering its initial investment, maintenance,
designed capacity, daily throughput, and designed useful life. However, construction cost for switching
the system is not incorporated in this study. Recommendations are provided by different groups, with
counties sharing several similarities to a certain extent.
Group 1: Counties for which dual stream recycling systems are recommended
Counties belong to this group are all suggested to utilize the dual stream recycling system, which
incurs a lower total cost compared to utilizing the single stream recycling system. Among these counties,
St. Lucie, Hillsborough, Manatee, Sarasota, and Volusia are all operated a DSR system currently.
Therefore, no changes are needed in these counties, unless the cost for extra sorting efforts in the MRFs
under the SSR system can be reduced in the future. For example, break-even point for the extra sorting
cost in Hillsborough County is $1/ton. This means that if the extra sorting cost occurred in the MRFs
under the SSR system is less than $1/ton, the SSR system is recommended; otherwise, the DSR system is
recommended. For Martin, Polk, Nassau, Marion, Levy, Lake, Indiana River, Escambia, Hernando,
Hamilton, Collier and Desoto Counties, it is recommended to switch to the DSR systems from their
currently operated SSR systems for the purpose of reducing the total cost. However, if the participation
rate of recycling under the SSR systems is much higher than that under the DSR system, it may be worthy
to operate the SSR system with a high cost. Moreover, in this group, several counties, including the Santa
Rosa, Liberty, Gadsden, Franklin, Citrus, Bay, and Columbia, currently do not provide any curbside
collection services to their residents. These counties are all rural counties with small population and
limited government budget. Assuming that there will not be a significant change in their participation rate
under either systems (SSR and DSR), DSR is recommended for cost-effectiveness for these counties.
Group 2: Counties for which single stream recycling systems are recommended
The results for this group have shown that the SSR systems are proven to be more cost-effective
compared to that of the DSR systems. The Counties of Highlands, Hardee, and Gulf, currently do not
36
provide the curbside collection services to their residents, and the results from the experiments have
showhn that the implementation of SSR within these systems would prove more effective than the DSR.
Several other counties including Miami-Dade, Seminole, Pinellas, Pasco, Orange, Okaloosa, Brevard,
Charlotte, Counties have switched their SWM collection system from the DSR to the SSR. For these
counties, the results have shown that continuing their operation with their current single stream recycling
system in the future may prove more beneficial. In addition, within these counties the impact of
participation rate is also significant meaning that the more efforts placed in the education programs to
improve the residents’ participation rates of recycling, the higher the recycling rate can be achieved.
Utilizing the proposed tool, the break-even points for the extra sorting cost for counties that belong to this
group may also be found and studied in detail. For example, in Pasco County, if the extra sorting cost
occurred in the MRF under SSR system increases to be higher than $28/ton, the DSR system is
recommended in this county.
Group 3: Inter-dependent Counties
In this group, performances of the single stream recycling systems and dual stream recycling
systems in all the inter-dependent counties are tested and compared. An inter-dependent county is
defined as one which either transfers materials to, or receives materials from, one or more of the counties
included in the study region. Figure 15 provides an example of inter-county dependencies, in which
Holmes, Calhoun, Wakulla and Leon Counties deliver waste to Jackson County. When the user selects a
county for experimentation in the userform of the developed tool, the counties with which the selected
county has inter-county dependencies are automatically added, and are enforced under all circumstances.
The inter-dependency situations considered here happen as a result of particular system organizations that
exist in the State of Florida SWM system where one or more counties do not have the necessary facilities
to complete the SWM lifecycle for the entirety of their generation, or where such arrangements are
economically advantageous.
37
Figure 15: An example of inter-county dependencies
Among those inter-dependent counties, the Counties of Alachua, Clay, Leon, Wakulla, Palm
Beach are currently running the dual stream recycling systems; the Counties of Broward, Duval, St. Johns,
Flagler, Putnam, Hendry, Lee, Osceola are processing recyclables in single stream, while the Counties of
Baker, Bradford, Gilchrist, Calhoun, Holmes, Jackson, Dixie, Jefferson, Madison, Taylor, Lafayette,
Suwannee, Sumter, Walton and Washington provide only drop-off service for residents and on-call
service for commercial units. As shown in Table 9, the cost of single stream recycling systems are found
to be significantly less when compared to that of the dual stream recycling systems if an inter-dependency
exist in the considered county or counties.
Table 9: Comparison of the SSR and DSR in terms of the total cost for the selected counties
Inter-dependent Counties SSR DSR
Alachua-Baker-Bradford-Clay-Gilchrist $82,892,224 $84,375,740
Calhoun-Holmes-Jackson-Leon-Wakulla $132,640,889 $140,793,565
Dixie-Jefferson-Madison-Taylor $6,925,080 $7,220,851
Duval-St. Johns $253,942,582 $261,185,035
Flagler-Putnam $27,044,217 $28,110,572
Hendry-Lee $78,471,332 $78,749,936
Lafayette-Suwannee $48,436,459 $50,049,575
Osceola-Sumter $45,405,636 $46,263,047
Walton-Washington $11,926,045 $11,917,982
Broward-Palm Beach-Okeechobee-Monroe-Glades $466,420,396 $480,010,345
38
4.3 Impacts of Participation Rate
As mentioned in Section 1, one major advantage of the SSR system is that the residents’ participation for
recycling may increase because of the reduced sorting efforts on the residents’ side. Hence, in the third set
of experiments, comparison of the SSR and DSR systems is conducted by considering the potential
impacts of residents’ participation rate. Results of experiments obtained for two selected counties are
presented as case studies as the following.
Case I: Leon County
Basic information of Leon County, regarding to its population, municipalities, FDEP permitted facilities,
franchised haulers, etc., has been introduced in Section 4.1 under the case study of Leon County for
bottleneck facility identification. Except for those information, it is known that Leon County is currently
operates a DSR program, with a 38% annual recycling rate. Experiments are carried out by increasing the
participation rate for recycling under SSR system from 40% to 70% while keeping the participation rate
for recycling under DSR system as 40%. Table 10 provides the detailed results.
Table 10: Comparison of the SSR and DSR systems in Leon County
SSR DSR Difference in
Total Cost
Percentage of
Change in Cost Participation
Rate (%)
Recycling
Rate (%)
Participation
Rate (%)
Recycling
Rate (%)
40 38.22 40 34.40 ($7,636,266.84) -5.53%
50 48.03 40 34.46 ($19,931,432.00) -14.41%
60 57.9 40 34.39 ($34,164,299.31) -24.73%
70 69.04 40 34.41 ($51,619,442.84) -37.37%
It is noted that increasing the participation rate from 40% to 70% leads to a significant
improvement on the recycling rate (34.64%) and a reduced total cost (37.37%) occurred under the SSR
system. This is because when the recycling rate increased, more recyclables are sent to the Marpan
Recycling MRF in Leon County while less waste are sent to the landfill located in Jackson County.
Therefore, the transportation cost decreased dramatically and these savings have covered the increase on
the processing cost due to the extra sorting efforts under the SSR system.
Case II: Hillsborough County
In the second case, Hillsborough County is selected. Hillsborough County currently operates a DSR
system with a 37.4% recycling rate. Particularly, it serves a population of 1,268,000 with five transfer
stations, five material recovery facilities, two waste-to-energy plants, and one landfill. Detailed
39
information regarding the generation units, facilities, and haulers in the Hillsborough County are provided
in Table 11.
Table 11: An overview of Hillsborough County Solid Waste Management System
Facility Type
Daily
Capacity
(tons/day)
Wimauma Facility TS 1000
Northwest Hillsborough SLF TS 1000
South County Transfer Station TS 1000
Alderman Ford Facility TS 1000
Hillsborough Heights Facility TS 1000
Recycle America of Tampa MRF 700
Jamson Environmental Inc. MRF 400
Metro Recycling - Tampa (MRF) MRF 700
Blue Monkey Recycling MRF 400
Generation Units
(tons/day) 5153
Tampa Materials Transfer &
Recycling WPF MFR 550
Franchised
Haulers
Waste Management Inc. Hills County Waste-To-Energy Plant WTE 1800
Republic Waste Services Mckay Bay Refuse-To-Energy WTE 1000
Waste Service Inc. Southeast County SLF(Picnic LF) LF 1800
As shown in Table 12, when the participation rate is increased from 40% to 50%, to 60%, and to
70%, the percentage of change in terms of total cost by switching the DSR to SSR becomes 0.7%, 5.2%,
7.8% and 8%, respectively. Moreover, recycling rate under SSR system is also increased from 37.44% to
66.99%. In other words, the recycling rate is improved under the SSR system with the expense of
increasing the total cost occurred throughout the SWM system. Therefore, in this county, a trade-off
between the recycling rate and increased total cost has to be considered when making the decision on the
recycling programs.
Table 12: Comparison of the SSR and DSR systems in Hillsborough County
SSR DSR Difference in
Total Cost
Percentage of
Change in Cost Participation
Rate (%)
Recycling
Rate (%)
Participation
Rate (%)
Recycling
Rate (%)
40 37.44 40 37.42 $1,731,108.90 0.78%
50 47.14 40 37.47 $11,596,664.99 5.23%
60 57.12 40 37.39 $17,441,211.02 7.86%
70 66.99 40 37.31 $17,834,339.87 8.08%
40
4.4 Fleet Utilization and Vehicle Routing Optimization
In the fourth set of experiments, the genetic algorithm based optimization model is running to provide
recommendations on fleet utilization and vehicle routing in different counties. Here, results obtained
through the experiments for Alachua County are selected as an example to be presented. Particularly,
there are 21 clusters (zip code areas) and 19,881 generation points (intersections), among which 10,232
generation points are associated with the generation units (i.e., single/multi residential units, commercial
units) in the county. These generation points are finally split into 358 sub-clusters based on the proposed
Voronoi-based algorithm, and each sub-cluster is visited by one truck in one trip. Figure 16 (a) provides a
map of the Alachua County, and (b) presents all the generation points in the county. Then zip code area
‘36203’ is selected and three sub-clusters belonging to this cluster are highlighted and presented in (c), (d),
(f), and (e), respectively.
The results obtained from the experiments have shown that the Waste Pro. Management is
demonstrated as the best hauler for the zip code area ‘36203’ in Alachua County. For the three sub-
clusters presented here, the recommendations for the truck’s visiting sequences are illustrated in the
figures. With these routes, the truck’s travelling distances are minimized, which are 35.37 km, 40.93 km,
35.07 km for sub-cluster 1, 2, and 3, respectively. Results are shown to depict the vehicle routing
capability of the proposed genetic-algorithm based optimization model for the truck drivers, and can be
applied to various counties within and outside the State of Florida.
(a) (b)
41
Figure 16: Results obtained from the proposed optimization module for the Alachua County
5. DISCUSSION AND CONCLUSIONS
In this study, a multi-objective agent-based simulation and optimization framework is developed to
investigate the recycling programs. To this end, agent-discrete modeling techniques are utilized to form a
novel hybrid simulation model, and Voronoi-based genetic algorithm and k-th nearest search algorithm
are combined to build an optimization model. Provided the necessary system data, the proposed
framework is capable of simulating the single stream recycling systems as well as its alternatives (i.e.,
dual stream recycling system) at the county and region level. The framework is also capable of providing
recommendations on vehicle routing and resource allocation for SWM systems with multi-objectives at
different scales (i.e., scalable from simple to complex, multi-county regional systems). A series of
experiments designed on a countywide basis in the State of Florida have been carried out, through which
the feasibility and effectiveness of the proposed framework is successfully demonstrated.
As mentioned in the Experiments and Results section, the results of the experiments carried out
provides evidences on the comparison of the performances of the SSR and DSR systems in terms of the
total cost occurred throughout the entire SWM system for all the 67 counties. Recommendations for each
(c)
(d) (e) (f) 1
2
3
42
county are contingent upon the accuracy of the data provided. It is noted that the performance of the
recycling program is highly related with the total waste processed in the system and the sorting cost in the
MRFs. Single stream recycling is proven to be more cost effective for most of the large counties that
have large populations and great daily waste generation rates, while dual stream recycling is shown to be
more effective for most of the rural counties with smaller populations and daily waste generation rates.
However, in either case, diversion systems require public education and outreach events and suitable
collection vehicles. The challenges associated with these resources are greatest in low-population areas,
which typically have low budgets and small fleets. Furthermore, bottleneck facilities are found to be the
major problems existed in the current SWM systems, which block the increase of the recycling rate in the
State of Florida. Therefore, this study recommends the strategic planning of capital projects, including
both the bottleneck facility extension and new facilities construction. For the fleet utilization and vehicle
routing problems, the proposed genetic algorithm based optimization model is able to provide a good
solution with a reasonable expense of computation efforts. However, the trucks’ traveling distances may
depend on various factors such as the drivers’ habits, road repairing, special activities, etc., making the
vehicle routing problem even more complicated and hard to control. A plan of linking the proposed
optimization model to an advance Global Position System (GPS), to adaptively guide the solid waste
collectors and drivers with the optimal routing, may be considered in the future.
The results of the experiments carried out indicate significant deficiencies in the operation of
existing recycling facilities, in terms of collection, transportation, facilities’ utilization and recycables’
marketability, especially in the counties with low-density and dispersed nature. Therefore, this study also
recommends the implementation of county or regional-level cooperative agreements, in which generation
units pool their waste streams in order to achieve the economies of scale associated with large, centralized
facilities, and the greater marketability of recyclable good facilitated by such facilities. However, this
proposed solution raises significant issues of transfer costs, and thus it is recommended that alternative
transfer mechanisms, such as railways and waterways, be evaluated during the formulation of interlocal
plans.
The future venues of this work concerns itself with the detailed analysis of residents’
participation rates of recycling, implementation of education programs, advanced sorting techniques for
MRFs under SSR system, amongst many others. A broader variety of applications and more precise
results may be achieved with the consideration of all these segments inherent to the solid waste
management systems.
43
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efficient?, Resources, Conservation and Recycling, 55(3), 325-334.
McDavid, J.C., 1985. The Canadian experience with privatizing residential solid waste collection
services, Public Administration Review, 45(5), 602-608.
Poser, I.V., and Awad, A.R, 2006. Optimal routing for solid waste collection in cities by using real
genetic algorithm, Information and Communication Technologies, ICTTA’06, 1, 221-226.
Rubenstein-Montano, B., and Zandi, I. 1999. Application of a genetic algorithm to policy planning: The
case of solid waste, Environment & Planning B: Planning & Design, 26, 893–907.
Savas, E.S., 1977. An empirical study of competition municipal service delivery, Public Administration
Review, 37(6), 717-724.
Savas, E.S., 1978. The institutional structure of local government services: a conceptual model, Public
Administration Review, 38(5), 412-419.
45
Savas, E.S., 1981. Intra-city competition between public and private service delivery, Public
Administration Review, 41(1), 46-52.
Schell, C., and Liu, A., 2009. Canada solid waste disposal equipment, Report submitted to U.S.
Commercial Service in Vancouver, Canada.
United States Census Bureau (USCB), 2011. TIGER/Line® Shapefiles and TIGER/Line® Files,
Available online: http://www.census.gov/geo/maps-data/data/tiger-line.html
Wang, J., 2006. All in one: do single-stream curbside recycling programs increase recycling rates? Thesis,
University of California, Berkeley, CA, USA.
46
APPENDIX A: WASTE COMPOSITION BY COUNTY
County C&D Aluminum and
Plastic Metal Paper
Food
Waste
Yard
Trash Glass
Process
Fuel Commercial
Alachua 19.03% 0.44% 10.69% 15.28% 0.03% 26.08% 1.73% 0.09% 26.62%
Baker 0.00% 0.69% 37.46% 48.85% 1.58% 0.00% 0.04% 0.00% 11.38%
Bay 14.27% 1.87% 47.23% 35.80% 0.00% 0.00% 0.18% 0.00% 0.65%
Bradford 0.27% 1.25% 51.66% 10.18% 0.45% 0.00% 12.65% 0.00% 23.53%
Brevard 21.91% 0.86% 13.90% 12.00% 0.06% 23.20% 1.91% 0.00% 26.17%
Broward 27.38% 0.69% 25.49% 29.23% 0.00% 5.77% 3.77% 0.00% 7.67%
Calhoun 0.00% 0.11% 79.08% 4.64% 0.00% 0.00% 1.16% 0.00% 15.01%
Charlotte 28.70% 1.18% 23.98% 23.52% 0.25% 5.49% 5.98% 0.00% 10.88%
Citrus 5.63% 1.33% 38.34% 17.35% 0.08% 14.24% 2.10% 0.00% 20.93%
Clay 0.33% 0.29% 15.91% 14.01% 0.24% 32.61% 2.36% 0.00% 34.23%
Collier 23.14% 0.40% 10.84% 20.81% 0.06% 18.93% 4.28% 0.00% 21.54%
Columbia 0.00% 1.47% 44.03% 24.21% 0.44% 0.00% 0.03% 0.00% 29.82%
Desoto 0.00% 2.39% 16.52% 33.69% 0.00% 22.37% 0.00% 0.00% 25.02%
Dixie 0.00% 0.07% 77.16% 3.85% 0.00% 0.00% 8.73% 0.00% 10.18%
Duval 37.45% 1.25% 23.92% 18.35% 0.11% 3.33% 1.10% 3.62% 10.88%
Escambia 1.21% 2.82% 52.31% 16.68% 0.15% 8.73% 0.77% 0.00% 17.33%
Flagler 24.62% 0.37% 39.61% 24.11% 0.25% 0.00% 3.79% 0.00% 7.24%
Franklin 0.39% 0.26% 10.57% 7.38% 0.00% 36.11% 3.29% 0.00% 42.00%
Gadsden 0.16% 0.48% 87.85% 11.51% 0.00% 0.00% 0.00% 0.00% 0.00%
Gilchrist 0.00% 2.68% 58.29% 23.92% 0.00% 0.00% 5.09% 0.00% 10.02%
Glades 0.00% 0.95% 82.14% 3.19% 0.00% 0.00% 0.00% 0.00% 13.72%
Gulf 0.00% 3.01% 81.42% 4.37% 0.00% 0.00% 0.00% 0.00% 11.20%
Hamilton 0.00% 1.83% 75.95% 9.36% 0.00% 0.00% 0.11% 0.00% 12.75%
Hardee 0.00% 14.90% 9.86% 13.51% 0.00% 30.79% 0.14% 0.00% 30.79%
Hendry 0.00% 1.85% 82.78% 13.43% 0.27% 0.03% 0.53% 0.00% 1.12%
Hernando 0.52% 1.07% 9.20% 35.22% 0.11% 18.78% 1.94% 0.00% 33.16%
Highlands 12.91% 1.87% 23.76% 56.53% 0.00% 0.00% 4.92% 0.00% 0.01%
Hillsborough 0.89% 0.70% 43.74% 14.91% 0.55% 4.89% 2.30% 9.02% 23.00%
Holmes 0.00% 0.81% 72.66% 20.14% 0.00% 0.00% 0.08% 0.00% 6.31%
Indian River 0.00% 0.59% 9.47% 12.17% 0.00% 9.24% 3.41% 27.11% 38.01%
Jackson 0.00% 0.53% 32.19% 66.49% 0.00% 0.00% 0.79% 0.00% 0.00%
Jefferson 1.03% 0.06% 53.36% 34.69% 0.00% 0.00% 7.30% 0.00% 3.55%
Lafayette 0.00% 0.00% 88.25% 5.97% 0.00% 0.00% 5.59% 0.00% 0.19%
Lake 23.11% 4.99% 19.02% 32.02% 0.17% 8.87% 1.81% 0.00% 10.02%
47
Lee 11.27% 0.84% 38.45% 16.53% 0.00% 12.34% 1.97% 0.00% 18.59%
Leon 45.57% 0.34% 10.54% 21.03% 0.06% 6.73% 2.45% 1.25% 12.04%
Levy 0.04% 0.86% 78.59% 15.25% 0.97% 0.00% 0.58% 0.00% 3.70%
Liberty 0.00% 2.08% 85.57% 0.07% 0.00% 0.00% 0.00% 0.00% 12.28%
Madison 0.00% 1.60% 41.22% 7.06% 0.00% 17.37% 0.00% 0.00% 32.75%
Manatee 8.85% 0.26% 9.45% 17.13% 0.07% 17.71% 3.01% 11.85% 31.67%
Marion 0.13% 0.83% 53.84% 19.42% 0.00% 11.88% 0.55% 0.00% 13.35%
Martin 11.19% 1.02% 3.80% 20.41% 0.03% 29.90% 2.93% 0.00% 30.72%
Miami-Dade 3.73% 0.38% 20.59% 34.23% 0.00% 9.77% 2.81% 5.02% 23.47%
Monroe 29.52% 0.19% 0.02% 11.17% 0.00% 0.00% 4.35% 27.37% 27.37%
Nassau 31.67% 0.42% 38.20% 22.59% 0.00% 0.00% 0.01% 0.00% 7.11%
Okaloosa 0.00% 0.75% 13.99% 16.40% 0.00% 31.54% 1.74% 0.00% 35.59%
Okeechobee 0.00% 0.54% 58.87% 6.55% 0.00% 16.65% 0.74% 0.00% 16.65%
Orange 21.01% 1.32% 16.76% 29.53% 3.37% 4.98% 4.52% 0.00% 18.50%
Osceola 2.27% 1.19% 22.23% 68.09% 0.00% 0.00% 1.30% 0.00% 4.92%
Palm Beach 14.67% 0.70% 17.10% 18.33% 0.00% 8.23% 1.68% 14.02% 25.28%
Pasco 28.46% 0.89% 17.80% 18.22% 0.00% 13.80% 3.15% 0.00% 17.67%
Pinellas 16.62% 0.40% 11.02% 14.85% 0.03% 25.82% 0.49% 0.00% 30.77%
Polk 0.56% 1.11% 33.87% 28.93% 0.16% 0.00% 3.11% 10.02% 22.23%
Putnam 0.99% 0.35% 40.89% 12.02% 0.19% 17.52% 3.03% 0.00% 25.02%
Santa Rosa 0.23% 0.31% 58.77% 18.36% 0.00% 8.64% 0.92% 0.00% 12.77%
Sarasota 40.56% 0.61% 13.31% 17.46% 0.59% 8.97% 6.25% 0.00% 12.25%
Seminole 26.87% 4.22% 5.90% 43.97% 0.17% 6.35% 5.28% 0.00% 7.25%
St. Johns 2.88% 0.33% 72.35% 22.08% 0.00% 0.00% 0.07% 0.00% 2.30%
St. Lucie 31.37% 0.47% 9.69% 11.18% 0.09% 22.07% 2.53% 0.00% 22.61%
Sumter 0.02% 4.13% 24.25% 56.63% 0.00% 0.00% 8.35% 0.00% 6.63%
Suwannee 0.00% 1.90% 71.05% 5.26% 0.00% 0.00% 0.16% 0.00% 21.64%
Taylor 0.59% 1.64% 54.85% 22.02% 0.00% 0.00% 0.20% 0.00% 20.71%
Union 2.68% 3.39% 64.00% 12.89% 0.00% 0.00% 0.00% 0.00% 17.03%
Volusia 45.82% 1.30% 17.16% 27.90% 0.13% 0.21% 3.08% 0.00% 4.41%
Wakulla 10.26% 0.42% 25.44% 57.31% 0.59% 0.00% 0.94% 0.00% 5.04%
Walton 0.00% 0.40% 43.19% 19.46% 0.00% 15.16% 0.00% 0.00% 21.79%
Washington 0.00% 0.00% 80.98% 3.46% 0.00% 0.00% 0.00% 0.00% 15.56%
48
APPENDIX B: MSW COLLECTION SERVICE SURVEY IN STATE OF FLORIDA
1. Please indicate with a check below which of the following recycling programs your city has adopted.
___ Curbside pickup
___ Single Stream Recycling
___ Dual Stream Recycling
___ Multiple Stream Recycling
___ Drop-off
2. If the curbside pickup service is adopted, which hauler is under contract with your city?
___ Services are provided by city government;
___ Services are provided by private haulers;
___ Waste Management Inc.
___ Waste Pro. Management
___ Republic Service Inc.
___ Veolia Environmental Services
___ Waste Services of Florida Inc.
___ Choice Environmental Service
___ Others;
If “others” is selected, please provide the contracted hauler name here: _________________________;
3. How much did you charge for the collection service per month per household? ________________;
Contact Information: ______________________________________________________________
49
APPENDIX C: SELECTED DOCUMENTS REVIEWED
1. Florida Department of Environment Protection, 2010. Table 2A: MSW Collected by Generator Type
in Florida by Descending Population Rank. Available on line:
<http://appprod.dep.state.fl.us/www_rcra/reports/WR/Recycling/2010AnnualReport/AppendixA/2A.
pdf>
2. Florida Department of Environment Protection, 2010. Table 5A: Final Disposition of Municipal Solid
Waste in Florida by Descending Population. Available online:
<http://appprod.dep.state.fl.us/www_rcra/reports/WR/Recycling/2010AnnualReport/AppendixA/5A.
pdf>
3. Florida Department of Environment Protection, 2010. Table 12B: Multi-Family Participation in
Recycling by Descending Population. Available online:
<http://appprod.dep.state.fl.us/www_rcra/reports/WR/Recycling/2010AnnualReport/AppendixB/12B.
pdf>
4. Florida Department of Environment Protection, 2010. Table 11B: Single-Family Participation in
Recycling By Descending Population. Available online:
<http://appprod.dep.state.fl.us/www_rcra/reports/WR/Recycling/2010AnnualReport/AppendixB/11B.
pdf>
5. Florida Department of Environment Protection, 2010. Table 13B: Commercial Participation in
Recycling By Descending Population. Available online:
<http://www.dep.state.fl.us/waste/quick_topics/publications/shw/recycling/swm_98/downloads/appen
dxb/table13b.pdf>
6. Florida Department of Environment Protection, 2010. Table 3B-1- Total Tons of MSW Materials
Collected and Recycled in Florida Minimum 4 out of 8 Materials by Descending Population Rank.
Available online:
<http://appprod.dep.state.fl.us/www_rcra/reports/WR/Recycling/2011AnnualReport/AppendixB/3B-
1.pdf>
7. Florida Department of Environment Protection, 2010. Table 3B-2- Total Tons of MSW Materials
Collected and Recycled in Florida Minimum 4 out of 8 Materials by Descending Population Rank.
Available online:
<http://appprod.dep.state.fl.us/www_rcra/reports/WR/Recycling/2011AnnualReport/AppendixB/3B-
2.pdf>
8. Florida Department of Environment Protection, 2010. Table 4B- Total Tons of Special Waste
Materials Collected and Recycled in Florida by Descending Population Rank. Available online:
<http://appprod.dep.state.fl.us/www_rcra/reports/WR/Recycling/2011AnnualReport/AppendixB/4B.p
df>
9. Florida Department of Environment Protection, 2010. Table 5B-1- Total Tons of MSW Materials
Collected and Recycled in Florida by Descending Population Rank. Available online:
<http://appprod.dep.state.fl.us/www_rcra/reports/WR/Recycling/2010AnnualReport/AppendixB/5B-
1.pdf>
10. Florida Department of Environment Protection, 2010. Table 5B-2- Total Tons of MSW Materials
Collected and Recycled in Florida by Descending Population Rank. Available online:
<http://appprod.dep.state.fl.us/www_rcra/reports/WR/Recycling/2010AnnualReport/AppendixB/5B-
2.pdf>
11. Florida Department of Environment Protection, 2010. Table 8B-1: Minimum Four out of Eight
Materials Recycled in Florida by Descending Population Rank. Available online:
<http://appprod.dep.state.fl.us/www_rcra/reports/WR/Recycling/2010AnnualReport/AppendixB/8B-
1.pdf>
12. Florida Department of Environment Protection, 2010. Table 8B-2: Minimum Four out of Eight
Materials Recycled in Florida by Descending Population Rank. Available online:
50
<http://appprod.dep.state.fl.us/www_rcra/reports/WR/Recycling/2010AnnualReport/AppendixB/8B-
2.pdf>
13. Florida Department of Environment Protection, 2010. Table 8B-3: Minimum Four out of Eight
Materials Recycled in Florida by Descending Population Rank. Available online:
<http://appprod.dep.state.fl.us/www_rcra/reports/WR/Recycling/2010AnnualReport/AppendixB/8B-1.pdf>
14. Florida Department of Environment Protection, 2010. Table 1A: County Municipal Solid Waste
Collection Per Capita By Descending Population Rank. Available online:
<http://appprod.dep.state.fl.us/www_rcra/reports/WR/Recycling/2010AnnualReport/AppendixA/1A.
pdf>
15. Florida Department of Environment Protection, 2011. Florida Recycling Availability by County and
Municipality. Available online:
<http://www.dep.state.fl.us/waste/quick_topics/publications/shw/recycling/2009AnnualReport/10B.p
df>
16. Waste Collection Procedures for Single Family and Duplex Homes. Miami-Dade County. Available
online: <http://www.citybeautiful.net/modules/showdocument.aspx?documentid=7078>
17. Solid Waste Collection Agreement, Brevard County. Available online:
<http://ww3.brevardcounty.us/swr/pdf/swagreement%20clean.pdf>
18. Bay County Website <http://www.co.bay.fl.us/waste/dropoff.php>
19. Brevard County Website <http://ww3.brevardcounty.us/swr/recycling.cfm>
20. Broward County Website
<http://www.broward.org/WASTEANDRECYCLING/UNINCORPORATEDAREAS/Pages/Service
AreaMapsandProviders.aspx>
21. Alachua County Website <http://alachuacounty.us/Depts/PW/Waste/recycling/Pages/Recycling.aspx.>
22. Broward County Website
<http://www.broward.org/WasteAndRecycling/Recycling/Pages/Default.aspx>
23. Charlotte County Website <http://www.charlottecountyfl.com/PublicWorks/SolidWaste/Recycling/>
24. Citrus County Website
<http://www.bocc.citrus.fl.us/pubworks/swm/recycling/materials_accepted.pdf>
25. Collier County Website <http://www.colliergov.net/Index.aspx?page=3289>
26. Flagler County Website <http://www.flaglercounty.org/index.aspx?NID=297>
27. Hendry County Website <http://www.hendryfla.net/special/recycle.htm>
28. Hernando County Board of County Commissioners, “A Guide for Recycling Materials & Drop off
Sites”. Available online:
<http://www.hernandocounty.us/utils/PDF/recycle/Brochure%20RECYCLING.pdf>
29. Hillsborough County Website <http://www.hillsboroughcounty.org/index.aspx?NID=1253>
30. Indian River County Website <http://www.ircwaste.com/Residential_Recycling.htm>
31. Lake County Website
<http://www.lakecountyfl.gov/departments/public_works/solid_waste/solid_waste_operations/hauler
_information.aspx>
32. Lake County Website
<http://www.lakecountyfl.gov/departments/public_works/solid_waste/solid_waste_programs/recyclin
g.aspx>
33. Lee County Website
<http://www3.leegov.com/gov/dept/SolidWaste/FindHauler/Pages/YourGarbageCompanyByZipCode
.aspx>
34. Lee County Website
<http://www.leegov.com/gov/dept/SolidWaste/Residents/Pages/RecycleableMaterials.aspx>
35. Leon County Website
<http://cms.leoncountyfl.gov/Home/Departments/OfficeofResourceStewardship/SolidWaste/Recyclin
gandEducationalServices>
51
36. Levy County Website <http://www.levycounty.org/cd_recycling.aspx>
37. Manatee County Website
<http://www.mymanatee.org/home/government/departments/utilities/recycling.html>
38. Marion County Website <http://www.marioncountyfl.org/solidwaste/divisions_recycling.aspx>
39. Martin County Website
<http://www.martin.fl.us/portal/page?_pageid=351,1216760&_dad=portal&_schema=PORTAL>
40. Martin County Website <http://geoweb.martin.fl.us/Solid_Waste/>
41. Miami-Dade County Website <http://www.miamidade.gov/publicworks/recycling.asp>
42. Miami-Dade County Website
<http://miami.about.com/gi/o.htm?zi=1/XJ&zTi=1&sdn=miami&cdn=citiestowns&tm=4&gps=340_
14_1311_563&f=00&su=p284.13.342.ip_p554.23.342.ip_&tt=2&bt=0&bts=0&zu=http%3A//www.c
o.miami-dade.fl.us/dswm/home.asp>
43. Miami-Dade County Website <http://www.miamigov.com/SolidWaste/pages/garbage/Garbage.asp>
44. Martin County Website
<http://ap3server.martin.fl.us:7778/portal/page?_pageid=351,530089&_dad=portal&_schema=PORT
AL>
45. Monroe County Recycling Brochure. Available online: <http://www.monroecounty-
fl.gov/DocumentCenter/Home/View/1540>
46. Monroe County Website <http://www.monroecounty-fl.gov/index.aspx?NID=69>
47. Okaloosa County Website <http://www.co.okaloosa.fl.us/dept_pw_resources_recycle.html>
48. Orange County Website <http://www.orangecountyfl.net/?tabid=371>
49. Palm Beach County Website <http://www.swa.org/site/collection_service/collection_service.htm>
50. Pasco County Website <http://www.pascocountyfl.net/index.aspx?NID=181>
51. Pinellas County Website <http://www.pinellascounty.org/utilities/recycle.htm>
52. Polk County Website <http://www.polk-county.net/subpage.aspx?menu_id=46&id=310>
53. Putman County Website <http://www.putnam-
fl.com/bocc/index.php?option=com_content&view=category&id=67&layout=blog&Itemid=84>
54. St. Johns County Website <http://www.co.st-johns.fl.us/SolidWaste/Recycle.aspx>
55. St. Lucie County Website <http://www.stlucieco.gov/solid_waste/curb_recycle.htm>
56. Santa Rosa County Website <http://www.santarosa.fl.gov/recycle/>
57. Okeechobee County Website <http://co.okeechobee.fl.us/solidwaste>
58. Monroe County Website
<http://fl-monroecounty.civicplus.com/DocumentCenter/Home/View/1540>
59. Monroe County Website <http://www.monroecounty-fl.gov/index.aspx?NID=69>
60. Sarasota County Website
https://www.scgov.net/ResidentialSolidWaste/Pages/ResidentialRecycling.aspx
61. Volusia County Website <http://volusia.org/recycle/>
62. Lee County Website / Solid Waste Collection Area 6: Boca Grande. Available online:
<http://www.leegov.com/gov/dept/solidwaste/FindHauler/Pages/Area6.aspx>
63. Lee County Website / Solid Waste Collection Area 5. Available online:
<http://www.leegov.com/gov/dept/solidwaste/FindHauler/Pages/Area5.aspx>
64. Lee County Website / Solid Waste Collection Area 4. Available online:
<http://www.leegov.com/gov/dept/solidwaste/FindHauler/Pages/Area4.aspx>
65. Lee County Website / Solid Waste Collection Area 3. Available online:
<http://www.leegov.com/gov/dept/solidwaste/FindHauler/Pages/Area3.aspx>
66. Lee County Website / Solid Waste Collection Area 2. Available online:
<http://www.leegov.com/gov/dept/solidwaste/FindHauler/Pages/Area2.aspx>
67. Lee County Website / Solid Waste Collection Area 1. Available online:
<http://www.leegov.com/gov/dept/solidwaste/FindHauler/Pages/Area1.aspx>
68. Collier County Website <http://www.colliergov.net/Index.aspx?page=3289>
69. Collier County Website <http://www.colliergov.net/index.aspx?page=3289>
52
70. Collier County Website
<http://www.colliergov.net/modules/showdocument.aspx?documentid=45441>
71. Charlotte County Website <http://www.charlottecountyfl.com/PublicWorks/SolidWaste/>
72. Hernando County Website <http://www.hernandocounty.us/utils/SolidWaste_Recycling/>
73. St. Lucie County Website <http://www.stlucieco.gov/solid_waste/curb_recycle.htm>
74. St. Lucie County Website <http://www.stlucieco.gov/solid_waste/waste_hauler.htm>
75. Highlands County Website
<http://www.hcbcc.net/departments/solid_waste/garbage_collection_schedule.php>
76. The City of Archer Website <http://www.archerfl.com/FAQs.htm>
77. The City of Alachua Website <http://www.cityofalachua.com/index.php/solid-waste>
78. The City of Hawthorne Website
<http://www.cityofhawthorne.net/Pages/HawthorneFL_DPW/Recycling>
79. The City of Micanopy Website <http://www.micanopytown.com/html/services.html>
80. The City of Newberry Website <http://www.ci.newberry.fl.us/waste-and-recycling-information/>
81. The City of Jacksonville Website <http://www.coj.net/departments/public-works/solid-
waste/faqs.aspx#day>
82. The City of Cocoa Website <http://www.cocoafl.org/index.aspx?NID=484>
83. The City of Cape Canaveral Website
<http://www.cityofcapecanaveral.org/vertical/sites/%7BCEAB0943-1F37-4E48-833E-
AFC3D9CA4058%7D/uploads/City_of_Cape_Canaveral_Single_Stream_Recycling_Program_Updat
ed_7-23-12.pdf>
84. The City of Cocoa Beach Website
<http://www.cityofcocoabeach.com/FlashHomePages/residents_home.html>
85. The Town of Indialantic Website <http://www.indialantic.com/?page_id=198>
86. The City of Melbourne Website <http://www.melbourneflorida.org/solid/>
87. The Town of Melbourne Beach Website
<http://www.melbournebeachfl.org/Pages/MelbourneBeachFL_PublicWorks/recycling>
88. The Town of Melbourne Village Website <http://www.melbournevillage.org/solid-waste-pickup.html>
89. The City of Palm Bay Website <http://www.palmbayflorida.org/finance/ucs/sanitation/index.html>
90. The City of Rockledge Website
<http://www.cityofrockledge.org/Pages/RockledgeFL_PublicWorks/recycling>
91. The City of Titusville Website <http://www.titusville.com/SectionIndex.asp?SectionID=53>
92. The City of West Melbourne Website <http://www.westmelbourne.org/index.aspx?NID=468>
93. The Town of India River Shores Website <http://www.irshores.com/Recycling-Calendar-for-
2008.html>
94. The Town of Orchid Website <http://www.townoforchid.com/Trash_and_Recycling.html>
95. The City of Vero Beach Website
<http://www.covb.org/index.asp?Type=B_LIST&SEC={0A007DC4-4151-4F67-9008-
5073DD43CB2C}>
96. The City of Fort Pierce Website <http://www.cityoffortpierce.com/Public%20Works/solidwaste.html>
97. The City of Stuart Website <http://www.cityofstuart.com/index.php/garbage/residential>
98. The City of Key Colony Beach Website <http://www.keycolonybeach.net/garbage.html>
99. The City of Key West Website
<http://www.keywestcity.com/egov/apps/services/index.egov?path=details&action=i&id=73>
100. The City of Sebring Website
<http://www.mysebring.com/index.asp?Type=B_BASIC&SEC={44F0B714-8122-4680-8751-
FE5EA5B113B1}>
101. The Bal Harbour Village Website <http://www.balharbourgov.com/government/departs.html>
102. The Town of Bay Harbor Islands Website <http://www.bayharborislands.org/content.aspx?id=30>
53
103. The Village of Biscayne Park Website
<http://www.biscayneparkfl.gov/index.asp?Type=B_BASIC&SEC={767F18F2-0285-49F9-8F3D-
7B60942A6FF1}>
104. The City of Cutler Bay Website <http://www.cutlerbay-fl.gov/garbage.php>
105. The City of Doral Website
<http://www.cityofdoral.com/index.php?option=com_content&view=article&id=152&Itemid=346>
106. The City of Golden Beach Website
<http://www.goldenbeach.us/index.php?option=com_content&view=article&id=69&Itemid=116>
107. The City of Hialeah Website
<http://www.hialeahfl.gov/dep/waste/recycling/curbside_recycling_collection.aspx>
108. The City of Homestead Website <http://www.cityofhomestead.com/index.aspx?nid=220>
109. The City of Key Biscayne Website
<http://www.keybiscayne.fl.gov/index.php?submenu=PublicWorks&src=gendocs&ref=SolidWaste_
Recycle_CHOICE2012&category=Public%20Works#garbage>
110. The City of Miami Beach Website
<http://www.miamibeachfl.gov/publicworks/sanitation/scroll.aspx?id=27294>
111. The North Bay Village Website
<http://www.nbvillage.com/Pages/NorthBayFL_DPW/TrashCollection>
112. The City of North Miami Website
<http://www.northmiamifl.gov/departments/publicworks/sanitation.aspx>
113. The Village of Palmetto Bay Website <http://www.palmettobay-fl.gov/content/garbage-and-
trash-collection>
114. The City of South Miami Website
<http://www.southmiamifl.gov/index.php?submenu=PubWorkEng&src=gendocs&ref=SolidWasteDi
vision&category=PubWorkEng>
115. The Town of Surfside Website
<http://www.townofsurfsidefl.gov/Pages/SurfsideFL_PublicWorks/solidwaste.pdf>
116. The Town of Loxahatchee Groves Website
<http://www.loxahatcheegroves.org/index.php?go=pages.page&pageId=56>
117. The Town of Manalapan <http://www.manalapan.org/index.aspx?nid=685>
118. The Village of North Palm Beach Website <http://www.village-
npb.org/index.asp?Type=B_BASIC&SEC={DCB0081E-4DB3-4D85-81B4-5D0A0D9581BC}>
119. The Town of Ocean Ridge Website <http://www.oceanridgeflorida.com/Other/New-Resident-
Information-0312.pdf>
120. The City of Pahokee Website
<http://www.cityofpahokee.com/Public%20Works/Public%20Works.htm>
121. The Town of Palm Beach Website
<http://www.townofpalmbeach.com/WebFiles/Public%20Works/Garbage%20&%20Recycling%20S
chedule.PDF>
122. The City of Palm Beach Gardens Website <http://www.pbgfl.com/content/74/2118/default.aspx>
123. The Town of Palm Beach Shores Website
<http://www.palmbeachshoresfl.us/TrashAndRecyclingSchedule>
124. The Village of Palm Springs Website <http://www.villageofpalmsprings.org/sanitation-
recycling.htm>
125. The Village of Tequesta Website <http://www.tequesta.org/index.aspx?nid=81>
126. The Town of Fort Myers Beach Website <http://www.fortmyersbeachfl.gov/index.aspx?nid=233>
127. The City of Sanibel Website <http://www.mysanibel.com/Departments/Public-Works-Including-
Utility-and-Parks-Maintenance/Solid-Waste-Information/How-to-Process-Garbage>
128. The Cooper City Website
<http://www.coopercityfl.org/index.asp?Type=B_BASIC&SEC={65742175-503D-43DE-9C0C-
492EF106A1DE}&DE={D4D97244-57CB-400E-8B27-C1D16C932BCE}>
54
129. The City of Coral Springs Website
<http://www.coralsprings.org/recycling/index.cfm?CFID=9423108&CFTOKEN=23772555>
130. The City of Davie Website <http://www.davie-fl.gov/Pages/DavieFL_Programms/garbage/index>
131. The City of Deerfield Beach Website <http://www.deerfield-beach.com/index.aspx?NID=227>
132. The City of Fort Lauderdale Website
<http://www.fortlauderdale.gov/public_services/trash/Residential_Solid_Waste_Services_Guide.pdf>
133. The City of Hallandale Beach Website <http://www.hallandalebeach.org/index.aspx?NID=204>
134. The Town of Hillsboro Beach Website
<http://www.townofhillsborobeach.com/images/File/2012/Miscellaneous/Choice%20Flyer.pdf>
135. The City of Hollywood Website <http://www.hollywoodfl.org/index.aspx?NID=421>
136. The Town of Lauderdale-By-The-Sea Website <http://www.lauderdalebythesea-
fl.gov/town/recycle.html>
137. The City of Lauderhill Website <http://lauderhill-fl.gov/garbage.asp>
138. The City of Miramar Website <http://www.ci.miramar.fl.us/publicworks/solidwaste/index.html>
139. The City of North Lauderdale Website
<http://www.nlauderdale.org/garbage_and_recycling/index.php>
140. The City of Parkland Website <http://www.cityofparkland.org/index.aspx?NID=342>
141. The City of Pembroke Pines Website <http://www.ppines.com/information/trash.html>
142. The City of Weston Website
<http://www.westonfl.org/Departments/PublicWorks/GarbageRecycle.aspx>
143. The City of Clewiston Website <http://www.clewiston-fl.gov/department/division.php?fDD=11-
25>
144. The City of Crystal River Website
<http://www.crystalriverfl.org/index.asp?Type=B_BASIC&SEC={D28F7F66-8C1A-4CE7-90AE-
477271537248}&DE={188B9191-2C78-498D-A8F3-57643D3D0911}>
145. The City of Inverness Website <http://www.inverness-fl.gov/index.aspx?nid=33>
146. The City of Jupiter Website <http://www.jupiter.fl.us/Engineering/Garbage-Recycling.cfm>
147. The Town of Lake Clark Shores Website
<http://www.townoflakeclarkeshores.com/service/garbage-service>
148. The Town of Lantana Website <http://www.lantana.org/page.asp?PageId=614>
149. Waste Pro of Florida, Inc. / City of Cocoa. Available online:
<http://www.wasteprousa.com/serviceareas-fl-cocoa.shtml>
150. Waste Pro of Florida, Inc. / Town of Grant Valkaria / Locations. Available online:
<http://www.wasteprousa.com/locations/CF/grantvalkaria>
151. Waste Pro of Florida, Inc. / Town of Grant Valkaria / Pickup Schedule. Available online:
<http://www.wasteprousa.com/locations/CF/grantvalkaria/regular-pickup-schedule/>
152. Waste Pro of Florida, Inc. / Florida-Alachua/Gainesville. Available online:
http://www.wasteprousa.com/serviceareas-fl-gainesville.shtml
153. Waste Pro of Florida, Inc. / Florida-Jacksonville. Available online:
http://www.wasteprousa.com/serviceareas-fl-jacksonville.shtml
154. Waste Management Inc. / Cocoa Hauling. Available online:
<http://www.wm.com/facility.jsp?zip=32922>
155. Republic Services / Volusia County. Available online:
<http://www.republicservicesvolusiacounty.com/Pages/ContactUs.aspx>
156. Waste Management Inc. / Brevard County / Residential Service. Available online:
<http://www.satellitebeachfl.org/Documents/HOME%20-%20WM%20Svs%20Info(1).dot>
157. Google Maps.
<http://maps.google.ca/maps?hl=en&cp=48&gs_id=0&xhr=t&q=waste+management,+Monroe,+Flor
ida,+United+States&qscrl=1&rlz=1T4TSCD_enCA497CA497&bav=on.2,or.r_gc.r_pw.r_qf.&biw=1
075&bih=625&ion=1&wrapid=tljp134737204369100&um=1&ie=UTF-8&sa=N&tab=w>
55
158. Google Maps.
<http://maps.google.ca/maps?hl=en&cp=48&gs_id=0&xhr=t&q=waste+management,+Monroe,+Flor
ida,+United+States&qscrl=1&rlz=1T4TSCD_enCA497CA497&bav=on.2,or.r_gc.r_pw.r_qf.&biw=1
075&bih=625&ion=1&wrapid=tljp134737204369100&um=1&ie=UTF-8&sa=N&tab=wl>
159. Google Maps.
<http://maps.google.ca/maps?hl=en&cp=48&gs_id=0&xhr=t&q=waste+management,+Monroe,+Flor
ida,+United+States&qscrl=1&rlz=1T4TSCD_enCA497CA497&bav=on.2,or.r_gc.r_pw.r_qf.&biw=1
075&bih=625&ion=1&wrapid=tljp134737204369100&um=1&ie=UTF-8&sa=N&tab=wl>
160. Google Maps.
<https://plus.google.com/118410972186027708792/about?gl=ca&hl=en#118410972186027708792/a
bout?gl=ca&hl=en>
161. Google Maps.
<https://plus.google.com/111558694095102643232/about?gl=ca&hl=en#111558694095102643232/a
bout?gl=ca&hl=en>
162. Google Maps.
<https://plus.google.com/108551437874514401889/about?gl=ca&hl=en#108551437874514401889/a
bout?gl=ca&hl=en>
163. Google Maps.
<https://plus.google.com/115921169417621570073/about?gl=ca&hl=en#115921169417621570073/a
bout?gl=ca&hl=en>
164. Google Maps.
<https://plus.google.com/109866417372268253166/about?gl=ca&hl=en#109866417372268253166/a
bout?gl=ca&hl=en>
165. Google Maps.
<https://plus.google.com/117182506133245996686/about?gl=ca&hl=en#117182506133245996686/a
bout?gl=ca&hl=en>
166. Google Maps.
<https://plus.google.com/102874462447608077993/about?gl=ca&hl=en#102874462447608077993/a
bout?gl=ca&hl=en>