intelligent support decision in sugarcane · pdf fileintelligent support decision in...

WCCA´2006 – Orlando – USA

Intelligent Support Decision in Intelligent Support Decision in Sugarcane HarvestSugarcane Harvest

State University of State University of PernambucoPernambuco –– RecifeRecife (Brazil)(Brazil)

Fernando Buarque de Lima Neto, PhD Fernando Buarque de Lima Neto, PhD

FlFláávio Rosendo da Silva Oliveiravio Rosendo da Silva OliveiraDiogo Ferreira PachecoDiogo Ferreira Pacheco

Amanda LeonelAmanda Leonel

WCCA´2006 – Orlando – USA © Fernando Buarque – [email protected]

AgendaAgenda

I.I. MotivationMotivation

II.II. Productivity Factors and Indicators Productivity Factors and Indicators

III.III. Decision Support Systems (DSS)Decision Support Systems (DSS)

IV.IV. IA: Genetic AlgorithmsIA: Genetic Algorithms

V.V. Abstract Model to Decision MakingAbstract Model to Decision Making

VI.VI. Computer tool, Simulation and ResultsComputer tool, Simulation and Results

VII.VII.ConclusionConclusion


Part IPart I

MotivationMotivation


Sugarcane strategic importanceSugarcane strategic importance

•• Sugarcane is a major source of Sugarcane is a major source of carbohydrates for human feeding carbohydrates for human feeding

•• Sugarcane is growing as a source of Sugarcane is growing as a source of renewable fuel (renewable fuel (e.g.e.g. 2020--30% of the 30% of the 23M vehicles of Brazilian fleet)23M vehicles of Brazilian fleet)

•• Tendencies are of marked increase of Tendencies are of marked increase of energy demands (both ways: fuel & energy demands (both ways: fuel & human feeding) human feeding)


Sugarcane harvestSugarcane harvest

•• Sugarcane harvest is complex due to Sugarcane harvest is complex due to nonnon--trivial decision process (many trivial decision process (many variables)variables)

•• Decision process encompass agronomical Decision process encompass agronomical and economical dimensionsand economical dimensions

•• Artificial intelligence can model Artificial intelligence can model sugarcane maturationsugarcane maturation [Lima Neto, 1998] [Lima Neto, 1998] [Madeiro [Madeiro et alet al, 2006] , 2006]


Objectives of this workObjectives of this work

1) To incorporate agronomical performance 1) To incorporate agronomical performance indicators into an Intelligent Decision indicators into an Intelligent Decision System to help decision makers (to System to help decision makers (to better deciding on sugarcane harvest) better deciding on sugarcane harvest)

2) To produce and test a prototype of a 2) To produce and test a prototype of a computer system that couple mills computer system that couple mills demands with acceptable agronomical demands with acceptable agronomical productivity levelsproductivity levels


Part IIPart II

Productivity Factors and IndicatorsProductivity Factors and Indicators


Mills computational environment Mills computational environment

•• All necessary All necessary ““horizontalhorizontal”” systems systems already installedalready installed

•• Huge amount of agricultural (historical) Huge amount of agricultural (historical) data availabledata available

•• Decision systems not necessarilyDecision systems not necessarily use use Artificial Artificial IntelligenceIntelligence

•• Productivity Factors and Indicators Productivity Factors and Indicators sometimes not appropriately monitored sometimes not appropriately monitored


Mills decision environment Mills decision environment

•• Large amount of cultivated areas to Large amount of cultivated areas to harvestharvest

•• Harvest season should be short to Harvest season should be short to reduce costsreduce costs

•• Sugarcane maturation (due to external Sugarcane maturation (due to external factors) is hard to forecastfactors) is hard to forecast

•• Harvesting at agricultural peaks Harvesting at agricultural peaks generates more profits generates more profits


Productivity factors x indicatorsProductivity factors x indicators

•• Factors:Factors:-- Can be controlled directlyCan be controlled directly

-- Exist in great numbersExist in great numbers

-- Are linked to timeAre linked to time

•• Indicators:Indicators:-- Can (only) be controlled indirectly, thru Can (only) be controlled indirectly, thru

factorsfactors

-- Exist (only) in reduced numbersExist (only) in reduced numbers

-- Are (also) linked to time Are (also) linked to time


Productivity factors Productivity factors

•• There are many productivity factors, for There are many productivity factors, for example:example:

--Cane variety (type);Cane variety (type);

--Soil/Topology;Soil/Topology;

--Climate;Climate;

--Sowing date;Sowing date;

--Age;Age;

etcetc


Productivity indicators Productivity indicators

•• There are many productivity indicators, There are many productivity indicators, for example:for example:

--PCC (sucrose);PCC (sucrose);

--TCH (biomass);TCH (biomass);

--Fiber (quality of).Fiber (quality of).


Part IIIPart III

Decision Support Systems Decision Support Systems (DSS)(DSS)


Decision Support Systems Decision Support Systems

•• Help on the decision making process of Help on the decision making process of semisemi--structured problemsstructured problems

•• Generally used on midGenerally used on mid--managerial levelmanagerial level

•• Users can select among possible Users can select among possible scenarios via decision dialoguesscenarios via decision dialogues

•• DSS should be friendly, fast and flexible DSS should be friendly, fast and flexible to consider daily basis variables, e.g. to consider daily basis variables, e.g. sugarcane demand for the day.sugarcane demand for the day.


Decision Support Systems Decision Support Systems

S

T

O

-Impact

-Scope

-AdHoc

-Frequency

-Detail

-Planning


Part IVPart IV

AI: Genetic AlgorithmsAI: Genetic Algorithms


Artificial Intelligence (in short) Artificial Intelligence (in short)

A computer system (HW / SW) that A computer system (HW / SW) that isisable to learn and store this able to learn and store this knowledge to a further situation knowledge to a further situation (that can also be a new one)(that can also be a new one)


Genetic Algorithms (in short)Genetic Algorithms (in short)

GA is GA is an AIan AI approach that is inspired in approach that is inspired in nature (evolution); that is, survival nature (evolution); that is, survival of the fittest of the fittest


Genetic AlgorithmsGenetic Algorithms

•• They are computational search techniques They are computational search techniques

•• Problem features are coded in Problem features are coded in ““genesgenes””

•• Problem candidate solutions are Problem candidate solutions are chromosomeschromosomes

•• The best solution is found thru evolution The best solution is found thru evolution within the population (maximization)within the population (maximization)

•• Some parameters are: pop size, features Some parameters are: pop size, features to be coded, evolution steps, mutation etcto be coded, evolution steps, mutation etc


Genetic AlgorithmsGenetic Algorithms

Processing cycle for a simple GA


Part VPart V

Abstract Model to Decision MakingAbstract Model to Decision Making


Why abstract modelsWhy abstract models

•• Understanding the problem is an Understanding the problem is an important part of its solutionimportant part of its solution

•• Sometimes a problem solution involves Sometimes a problem solution involves resolving conflicts (e.g. biomass vs. resolving conflicts (e.g. biomass vs. sucrose)sucrose)

•• Easier to visualize variable correlationsEasier to visualize variable correlations

•• Easier to reconfigure solutionsEasier to reconfigure solutions

•• Roadmap to computer implementationsRoadmap to computer implementations


Abstract models to decisionsAbstract models to decisions

Given a decision maker Given a decision maker mmkk , a decision , a decision problem problem ppkk and a sought decision and a sought decision ddkkcomponent of decision space component of decision space DDpp..Lets assume that each decision is composed Lets assume that each decision is composed of components of components ccjj , so that , so that ddkk = {c= {c11,c,c2,2,...,...,ccnn}, }, Lets assume that each component is Lets assume that each component is composed of attributes composed of attributes aaii , so that , so that ccjj = (a= (a11,a,a2,2,...,a...,ann) ) Note that components can be either Note that components can be either quantitative or qualitativequantitative or qualitative


Abstract models to decisionsAbstract models to decisions

Decision Space over Problem P encompassing decisions, components and their attributes


Relevance of decision componentsRelevance of decision components

In this way, a suitable solution would be to In this way, a suitable solution would be to search thoroughly among the possible search thoroughly among the possible solutions bysolutions by•• Assessing the relevance of every Assessing the relevance of every

component of each decisioncomponent of each decision

∑∑

=

== n

i i

n

i i

wafw

CR iij

0

0)*( )(

)( Regarding attribute:Regarding attribute:ffii(a(aii) Mapping function) Mapping functionW = {w1,w2,...,W = {w1,w2,...,wnwn} }

weightsweights


Maximizing overall relevance Maximizing overall relevance

Minimizing overall penaltyMinimizing overall penalty

Evaluating a candidate decisionEvaluating a candidate decision

∑==

n

j CRdF jk 0 )()(

∑ ==

n

j CRdFj

k 0 )(1

)(


1.1. Gather candidate decisions (ANN)Gather candidate decisions (ANN)

2.2. Define components Define components andand attributesattributes

3.3. Set validity criteriaSet validity criteria

4.4. Proceed the search (gDSS) Proceed the search (gDSS)

5.5. Evaluate decisionEvaluate decision

6.6. Finish or reFinish or re--start from step 4.start from step 4.

Steps of the model (review)Steps of the model (review)


Part VIPart VI

Computer tool, Simulation and Computer tool, Simulation and ResultsResults


• An artificial neural network engine was utilized to forecast productivity indicators

• A DSS tool was specially produced: Genetic Decision Support System (gDSS)

• In the data set used there are 418 candidate plots to be harvested (at different stages of maturation)

Computer tools and data setComputer tools and data set


To automatically find a suitable set of plots that are good to be harvested according to the decision maker standards

The decision problem simulatedThe decision problem simulated


• Each decision here represents a set of plots • Individual plots are components of candidate

decisions to be made• PCC and TCH are the attributes of each

component (of every plot)• A suitable decision has to satisfy the validity

criteria of the decision maker• Decisions are to be selected according to

their relevance (considering their components and attributes)

Applying Applying gDSSgDSS to the problemto the problem


Evaluation of attributesEvaluation of attributes

)**(**

)(PCCAreaTCHMAX

PCCAreaTCHPCCf iiiipcc=

)**(**

)(FiberAreaTCHMAX

FiberAreaTCHFiberf iiiiFiber=

Decision maker has to inform his/her:a) Business preferences: W = { w-fiber,w-pcc}b) needs: {desired ton of sugarcane, Maximum Area, Minimum

PCC, Minimum Fiber}

Evaluating decision for this problemEvaluating decision for this problem


wwwPCCfwFiberf

CRPCCFiber

PCCipccFiberiFibraj +

+=

*)(*)()(


Evaluation of componentsEvaluation of components



Evaluation of decisionEvaluation of decision

)(0)( ∑ =

=n

j jcdF Rk


Desired scenario:• Validity criteria

• PCC (minimum) = 16• Fiber (minimum) = 15• TCH (target) = 650 T• Area (maximum) = 10 plots

• Weights of attributes:• wpcc = 10• wfiber = 5

Simulation: gDSS parameters Simulation: gDSS parameters


16.101216.101215.132415.1324649.8212649.821222, 314, 290, 335, 22, 314, 290, 335, 194, 147194, 147

gDSS gDSS (this work)(this work)

16.543116.543115.834915.8349667.0466667.046626, 34, 56, 102, 26, 34, 56, 102, 131, 169, 199, 131, 169, 199, 365, 385, 404365, 385, 404

[Pacheco, [Pacheco, 2006]2006]

16.547816.547815.837615.8376649.0026649.002634, 56, 102, 169, 34, 56, 102, 169, 199, 238, 365, 199, 238, 365, 385, 404385, 404

Manual Manual selectionselection

Avg. Avg. PCC PCC

Avg. Avg. FiberFiber

TCHTCHPlotsPlots--IDIDMethodMethodOverall Performance Comparison among distinct Decision Methods

Result comparisonResult comparison


Part VIIPart VII

ConclusionConclusion


ConclusionConclusion

Contributed DSS is a great advance Contributed DSS is a great advance when compared current harvest decision when compared current harvest decision process because:process because:•• SpeedSpeed--up the decision processup the decision process•• Reduce the number of plots selected with Reduce the number of plots selected with

neither compromising quality nor biomassneither compromising quality nor biomass•• Help on reducing human error Help on reducing human error

Contributed approach allows reContributed approach allows re--runs runs generating different suggestions and generating different suggestions and distinct scenariosdistinct scenarios


ReferencesReferencesDiogo Pacheco. Diogo Pacheco. An Intelligent Decision Support System for Agriculture HarvestAn Intelligent Decision Support System for Agriculture Harvest (in (in Portuguese), Technical Report presented as Graduation Monograph Portuguese), Technical Report presented as Graduation Monograph to Department to Department of Computing Systems of Computing Systems –– Polytechnic School of Engineering Polytechnic School of Engineering –– Pernambuco State Pernambuco State University, 2006.University, 2006.Efrain Turban. Efrain Turban. Decision Support Systems and Expert SystemsDecision Support Systems and Expert Systems, 4th. , 4th. EditionEdition , , PrenticePrentice--HallHall InternationalInternational EditionsEditions. New Jersey, USA, 1995. . New Jersey, USA, 1995. Fernando Buarque de Lima Neto. Fernando Buarque de Lima Neto. Managerial Decision Support, based on Artificial Managerial Decision Support, based on Artificial Neural NetworksNeural Networks (in Portuguese), Master Dissertation presented to Department of (in Portuguese), Master Dissertation presented to Department of Informatics, Federal University of Pernambuco, Recife, Brazil, 1Informatics, Federal University of Pernambuco, Recife, Brazil, 1998.998.Randy L. Randy L. HauptHaupt and Sue E. and Sue E. HauptHaupt. . Practical Genetic AlgorithmsPractical Genetic Algorithms. 2nd ed. Wiley. 2nd ed. Wiley--InterscienceInterscience, 2004., 2004.Simon Simon HaykinHaykin. . Neural Networks Neural Networks –– A Comprehensive FoundationA Comprehensive Foundation. Prentice. Prentice--Hall Hall International Editions. New Jersey, USA, 1994.International Editions. New Jersey, USA, 1994.Ralph Sprague Jr. and Hugh J. Watson. Ralph Sprague Jr. and Hugh J. Watson. Decision Support for Management.Decision Support for Management.PrenticePrentice--Hall International Editions. New Jersey, USA, 1996.Hall International Editions. New Jersey, USA, 1996.Stuart Russell and Peter Stuart Russell and Peter NorvigNorvig. . Artificial Intelligence: A Modern Approach.Artificial Intelligence: A Modern Approach.2nd 2nd Edition. Edition. PrenticePrentice--HallHall InternationalInternational EditionsEditions. New Jersey, USA, 2003.. New Jersey, USA, 2003.


Thank you !Thank you !

Fernando Buarque [email protected]

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