decision-making in project management

7/29/2019 Decision-Making in project management

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Paper and Poster presented at:EnginSoft International Conference, Verona, Italy, 20-21 Oct. 2011.

ENGINSOFT Newsletter Year 8, No 4, Winter 2011.

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Reconsidering the Multiple Criteria

Decision Making Problems of Construction

Workers; Using Grapheur

A.Mosavi1, M.Azodinia2, Kasun N. Hewage1, Abbas S. Milani1, M.Yeheyis1,

1University of British Columbia – Okanagan, School of Engineering, 3333 University Way, Kelowna, BCV1V1V7, Canada.

2University of Debrecen, Faculty of IT, 4033 Debrecen, Hungary.

Summary

We are dealing with a series multiple criteria decision making problems and analysis related to Canadian

construction projects including waste management, productivity improvement, human and IT factors, emergy

based lifecycle, and process optimization.

The urgent increasing of using IT in construction projects has been one way to improve the process of solving

our problems [2]. The construction project managers have to make tough decisions. They are considering

different tools of IT and would like to invest on getting better data analysis tools for enhancing their decisions.

However making critical decisions for the complicated and multiple criteria problems of construction projects inwhich huge amount of data are involved is not a simple task to do. As the data-sets of our problems are often

huge they can not easily be handled with the traditional means of data analysis. In order to better manage thedata collected and make the most of our data-sets we utilized the advanced interactive visualization tools

provided by Grapheur and reconsidered our problems. Here the idea for solving the multiple criteria decision

making problems is to visually model and clarify the whole dimension of problems. The effectiveness and

performance of the interactive visualizations, made by Grapheur, are evaluated along with a number of our

study cases related to construction workers. As the main result, the 7D plots and the option of sweeping throughdata have been found very useful for our applications. The achieved hidden information through Grapheur’s

visualization tools would enhance our further decisions.

Keywords

Building construction workers, IT usage in construction projects, reactive business intelligence, reactive search,

Grapheur, multi-objective optimization, multiple criteria decision making, interactive visualization, multi-

dimensional plots, 7D graphs, sweeping through data

Introduction to Grapheur

Grapheur is a data mining, modeling and interactive visualization package implementing the Reactive Business

Intelligence approach [3] which connects the user to the software through automated and intelligent self-tuning

methods on the basis of visualization. The principles of Grapheur were originated from researches on Reactive

Search Optimization [4]. The user friendly and innovative interface of provided visualization, via an interactive

multi-objective optimization, facilitates the process of making tough decisions. Grapheur is a handy and simple

tool in which frees the mind from software complications and concentrates on mining the useful information indata. It puts the user in an interactive loop, rapidly reacting to first results and visualizations to direct the

subsequent efforts, in order to suit the needs and preferences of the decision maker.





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The Reactive Search is utilized within Grapheur to integrate some machine learning techniques into search

heuristics for the visualization complex optimization problems and interactive decision making accordingly. InReactive Search for self-adaptation in an autonomic manner, we benefit from the past history of the search and

the knowledge accumulated while moving in the configuration space [4].

Grapheur sample visualizations

In one of the building construction projects a number of workers were surveyed with questionnaires and

observations [1]. Each row of our data-set is a construction worker with the corresponding columns,characterized by a series of parameters which are the ID and photo of each person, work time, looking for materials, looking for tools, specialization, moving , instruction, idleness and the other characteristics of the

construction workers. The primary result of our survey clearly notes the urgent need for training programs to

improve workers’ skill levels. However the decision-making on how and with what rate the training programs

should be arranged is not a simple task and it has to be considered from different perspectives and criteria. In

order to learn how the training programs would affect team efficiencies, team spirit , and team perceptions of supervision, Grapheur [5], the flexible and powerful Business Intelligence and Interactive Visualization [3] is

utilized. With the aid of provided data mining and visualization some useful and hidden information are

achieved which would enhance the process of solving the multiple criteria decision making problems of our case. After clarifying the dimension of the problem and finding out the relation between involved parameters

and objectives, the effective decisions are easier made.

1. Supporting the decisions on workers’ skills

Here the idea for solving the multiple criteria decision making problems is to visually and effectively model the

problems and clarify the whole dimension of them. For instance we are trying to find out with which rate and

how, the workers’ level of skills should grow in order to maintain their performance with regard to team perceptions of supervision. In order to study a part of this problem, we are considering the similarity map and

the parallel filters for optimizing the idleness characteristic of the workers. The related multidimensional plot

of the networks is created based on the collected data from the workers. The color code represents the

specialization of the workers and the size of the bubbles is proportional to the idleness of workers. In our similarity map of the graphical visualization, the gray level of the edges and the generated clusters provide

valuable information for the decision maker. In the following figure and with the provided video the capability

of the similarity map for an effective clustering of the workers into meaningful clusters is illustrated (figure 1

right side).

The parallel filters (Figure 1 left side) are other useful tools for optimization. The usefulness of parallel filters in

reducing the complexity from the process of decision making is evaluated. We start from the matrix of work time in a multidimensional space while aiming at filtering particular workers and examining their performance

within a particular group e.g. those who have had maximum idleness characteristic.

Figure 1: Parallel filter (left side) and similarity map (right side)





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2. Displaying the precise condition of each construction worker

For the complete visualization of the condition of each construction worker over all parameters, the colored

bubble chart is selected. In the following figure (left side), the colored bubble chart shows work time versus specialization for each worker. The color code and the size of the bubbles represent looking for material characteristic and the idleness status of the workers respectively. Additionally the shape of the bubbles displays

the looking for tools characteristic of the workers.

In this graph we have found clustering tool very useful for a deep understanding of the different groups of workers. In this case workers could be grouped according to the given characteristics. After grouping, one

prototype case for each cluster is visualized which is indeed a very effective way of compressing the

information and concentrating on a relevant subset of possibilities.

Figure 2: Bubble chart (left side) and sweeping through data in the bubble chart (right side)

3. Sweeping though different characteristics of workers; tracking and

examining the problem with the aid of animated graphs

In the previous graph (Figure 2 left side) the relations between work time, specialization, idleness status, looking for materials, and looking for tools characteristics of the construction workers were visualized. Moreover

sweeping though data and studying the generated animations on sweeping is an effective tool for further

visualization along with advancing a particular objective. For instance in our next visualization experience the

previous graph is reconsidered by sweeping though looking material , Idleness and skill level as the time

advances (illustrated in Figure 2 right side).

4. Analyzing a particular cluster of workers and their characteristics;

sweeping through skill level and team perception of supervision

In a new created bubble graph, figure 3 right side, the idleness and specialization characteristic of a cluster of

four workers is associated with the size and the color of the bubbles relatively. Here by sweeping through team perception of supervision and the level of specialization of field workers in our building construction project, theachieved information from a limited cluster of workers can clarify the problem with more details in different

scenarios. For instance when the skill level of the workers and the team perception of supervision are monthly

increased relatively by the rate of 10% and 5% within a year, the idleness characteristic is smoothly monitored.

We can also play the resulted animation in smooth mode and track the past values (they appear in a lighter tone

in the background of the plot), in order to focus on the changes which occur according to morning and afternoonworking shifts.





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Figure 3: 7D plot of data (left side) and sweeping through data (right side)

5. Providing a reliable way to find the most productive workers With the aid of a 7D plot the characteristics associated with the productivity come to our consideration within a

single graph. In our case the size, the color and the shape of the bubbles relatively displays the specialization,

the moving , and the following the instructions characteristic of the workers. Moreover the blinking feature

displays the idleness characteristic of the workers who have been idle less than 100 hours (Figure 3 left side).

Discussion and ConclusionsAlong with our study cases the aspects of data mining, modeling, and visualization the data related to

construction workers utilizing Grapheur are considered and briefly presented in this short article. We made the

most of IT applications via newly implemented data mining and visualization tools of Grapheur. Consideringthe ability of Grapheur, the interesting patterns are automatically extracted from our raw data-set via data

mining tools. Additionally the advanced visual analytical interfaces are involved to support the decision maker

interactively. With the further feature of Grapheur such as parallel filters and clustering tasks, the managers cansolve multi-objective optimization problems as it amends previous approaches. Furthermore the animations of

sweeping through data and advanced visualizations including 7D plots accomplish managers and enable them to

screen the data at their consulting room making decision interactively.

In one of our study cases Grapheur provided a widespread view on how the throughput of the whole project

would be affected by the increasing workers’ specialization and supervision. We swept through different

characteristic of workers in order to examine the whole dimensions of the problem. For instance, we assumed

that the problem of having high level of idleness within the workers might be solved by increasing the

supervision and team perception of supervision. For this reason workers are carefully clustered and analyzedwith regard to their level of

idlenessand

supervision. In this particular case, Grapheur has been a facile tool in

modeling the problem with the aid of a 7D plot . Once a 7D plot is created the problem could be visually

analyzed from seven different perspectives simultaneously. In other words a convenient way of concentrating

on our objectives and further decision making is provided by simply observing the size, the color, the shape, andthe blinking of the bubbles. Moreover utilizing further visualization options such as similarity maps, parallel

filters, and clustering would support making a confident decision.

For our future studies aiming at making easier and faster decisions we will reconsider our problems this time

with the aid of a developed issue of Grapheur called LIONsolver [8], Learning, and Intelligent OptimizatioNwhich is capable of learning from human feedback and previous attempts while benefiting from the Grapheur

visualization tools.





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References

[ 1]

Hewage K.N., Gannoruwa A., Ruwanpura J.Y. (2011), Current Status of Factors Leading to TeamPerformance of On-Site Construction Professionals in Alberta Building Construction Projects, Canadian

Journal of Civil Engineering.

[ 2] Hewage K.N., Ruwanpura J.Y., Jergeas G.F. (2009). IT Usage in Alberta’s Building Construction

Projects: Current Status and Challenges, Journal of Automation in Construction.

[ 3] Roberto Battiti and Mauro Brunato, Reactive Business Intelligence. From Data to Models to Insight,

Reactive Search Srl, Italy, February 2011.

[ 4] Battiti, Roberto; Mauro Brunato; Franco Mascia (2008). Reactive Search and Intelligent Optimization.

Springer Verlag.

[ 5] Battiti, Roberto; Mauro Brunato (2010). "Grapheur: A Software Architecture for Reactive and

Interactive Optimization, Proceedings Learning and Intelligent OptimizatioN LION 4, 2010, Venice, Italy.

[ 6] Battiti, Roberto; Andrea Passerini (2010). "Brain-Computer Evolutionary Multi-ObjectiveOptimization (BC-EMO): a genetic algorithm adapting to the decision maker." (PDF). IEEE Transactions on

Evolutionary Computation.

[ 7] http://www.LIONsolver.com/

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