A genetic algorithm for period vehicle routing problem with practical application
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UNIVERSIDADE FEDERAL DO CEARÁ PRÓ-REITORIA DE PESQUISA E PÓS-GRADUAÇÃO PROGRAMA DE MESTRADO EM LOGÍSTICA E PESQUISA OPERACIONAL. A Genetic Algorithm for Period Vehicle Routing Problem with Practical Application. José Lassance de Castro Silva Felipe Pinheiro Bezerra CYTEDHAROSA 2012.

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A Genetic Algorithm for Period Vehicle Routing Problem with Practical Application

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A genetic algorithm for period vehicle routing problem with practical application

UNIVERSIDADE FEDERAL DO CEARÁ

PRÓ-REITORIA DE PESQUISA E PÓS-GRADUAÇÃO

PROGRAMA DE MESTRADO EM LOGÍSTICA E PESQUISA OPERACIONAL

A Genetic Algorithm for Period Vehicle Routing Problem with Practical Application

José Lassance de Castro Silva

Felipe Pinheiro Bezerra

CYTEDHAROSA 2012


Outline

Outline

  • MotivatingProblem

  • ProblemDefinition

  • SolutionMethodAproach

  • ComputationalExperiments

  • Conclusionsand Future ResearchDirections


Motivating p roblem

MotivatingProblem

  • WholesalerDistributor

  • Ice cream and ice pops division

  • Sales team

  • Marketing mix:

    • Product

    • Pricing

    • Promotion

    • Placement

Practicalapplication:


Motivating problem

MotivatingProblem

Practicalapplication:

  • SALES TEAM ROUTINE AT CUSTOMER STORE

  • Observe visibilityandpromotionelements

  • Inspectequipments (freezers)

  • Clean theequipmentsandrearrangetheproductsinsidethem

  • Remove strangeproducts

  • Analysesupply, assortmentandprices

  • Negotiateimprovementsandorders

  • Placeorder


Motivating problem1

MotivatingProblem

Practicalapplication: Currentsolutionmethod


Motivating problem2

MotivatingProblem

  • Advantages:

    • Out of route serving

    • Intuitive inclusion of new customers

    • Sales representative´s familiarity with territory

Practicalapplication: Currentsolutionmethod


Motivating problem3

MotivatingProblem

  • Drawbacks:

    • No tour definition

    • Replanning cost (time)

    • Learning curve

    • Unable to handle customer with multiple service frequence demand

Practicalapplication: Currentsolutionmethod


Motivating problem4

MotivatingProblem

  • Predefinedfrequence a regularity

  • Routeoptimization

    • Savetravel time

    • Increasesalesoportunity

    • Minimize travelcostsandrisks

  • Fastandeasyto use

  • Operationalrestrictions

    • Team size

    • Daily workload

Practicalapplication: Considerations


The periodic vehicle routing problem pvrp

The PeriodicVehicleRoutingProblem (PVRP)

  • Given:

    • a set ofcustomerswithknowndemandsandvisitfrequencies;

    • a set of schedule options for eachcustomer;

    • a planningperiodofmultipledays;

    • a homogeneousfleetofvehicleswithlimitedcapacity;

    • thelocationofthecustomersandthe central depot (wherealltrips must start andend);

    • the complete network wihtknownarccosts.

  • Find:

    • A set ofroutes over theplannigperiod.

  • Objective:

    • Minimize the global visiting cost.


The periodic vehicle routing problem pvrp1

The Periodic Vehicle Routing Problem (PVRP)

(BALDACCI et al., 2011)

1 vehicle

30 unitsofcapacity


The periodic vehicle routing problem pvrp2

The Periodic Vehicle Routing Problem (PVRP)

  • Select a visit schedule for eachcustomer;

  • Define thecustomersthatshouldbevisitedbyeachvehicleoneachday;

  • Routethevehicles for eachday.

Threesimultaneousdecisions:

It´s a generalizationoftheVRP: NP-Hard.


Solution method aproach

SolutionMethodAproach

  • Holland (1975)

  • Metaheuristic

  • Natural selection

  • Populationbased

  • Cromossomes/individuals

  • Recombinations

  • Fitness

GeneticAlgorithms: Concepts


Solution method aproach1

SolutionMethodAproach

Genetic Algorithms: Basic pseudocode

Begin

generateinitialpopulation

evaluate fitness ofeach individual

Whilestop criteriaisnottruedo

proceed crossovers

proceedmutations

evaluate new individuals

selectindividualstoreplaceandtheirreplacements

update stop criteria

End

returnbestsolution

End


Solution method aproach2

SolutionMethodAproach

  • Solutionrepresentation

  • Fitness function

  • Populationcontrol

  • Selectionmethod

  • Geneticoperators

  • Use ofhibridization

  • Stop criteria

  • Parametersdefinition

GeneticAlgorithms: Key points


Solution method aproach3

SolutionMethodAproach

  • Solutionrepresentation

    • Grand Tour

    • No tripdelimiters

    • Prins (2004), Chu et al. (2004) e Vidal et al. (2012)

Proposedgeneticalgorithm:

(VIDAL et al. 2012)


Solution method aproach4

SolutionMethodAproach

  • Individuals evaluation: Split algorithm (PRINS 2004)

Proposedgeneticalgorithm:

(PRINS, 2004)


Solution method aproach5

SolutionMethodAproach

  • Original crossover operator

Proposed genetic algorithm:


Computational experiments

Computational experiments

Benchmark instancestesting:

Resultson benchmark instances.

STATE-OF-THE-ART METHODS

TanandBeasley (1984)- TB

ChristofidesandBeasley (1984)- CB

Chaoet al. (1995)- CGW

Cordeau et al. (1997)- CGL

Alegre et al. (2007)- ALP

Hemmelmayret al. (2007)- HDR

Baldacciet al.(2011)- BLD

Vidal et al. (2012) - VDL


Computational experiments1

Computational experiments

Benchmark instancestesting:

Averagecomputationalcost in minutes

Source: Vidal et al. (2012)

STATE-OF-THE-ART METHODS

Cordeauet al. (1997)CGL

Alegre et al. (2007)ALP

Hemmelmayret al. (2007)HDR

Chaoet al. (1995)CGW

Vidal et al. (2012) VDL


Computational experiments2

Computational experiments

  • Fair results

  • Lowcomputationalcosts

Benchmark instancestesting:


Computational experiments3

Computationalexperiments

  • Briefing

    • 629 Stores

    • 7 salesrepresentatives

    • Weeklyvisits, frommondaythroughfriday

    • 5 schedule options, except for 36 customers

    • Service time: 15 minutes

    • Maximumdailyworkload: 8 hours (480 minutes)

    • Travelspeed: 30km/h

Practicalapplication: Solutionmethodapplied


Computational experiments4

Computational experiments

Practicalapplication: Solutionmethodapplied


Computational experiments5

Computational experiments

  • Adjustments:

    • Demand = service time

    • Restrictions = dailyworkload in mimutes

    • Travel time

    • Penalties for notusingevery“vehicle” daily

Practicalapplication: Solutionmethodapplied


Computational experiments6

Computational experiments

Practicalapplication: Solutionmethodapplied

Distance savings over planning period

Average daily workload composition per salesman (minutes).


Computational experiments7

Computational experiments

  • Initialfindings:

    • Downtimeawareness

    • Trade-off betweensavingsandworkloadbalancing

    • “Howmuch does theworkloadbalancingcost?”

Practicalapplication: Solutionmethodapplied


Computational experiments8

Computational experiments

Practicalapplication: Solutionmethodapplied

.


Computational experiments9

Computationalexperiments

Practicalapplication: Solutionmethodapplied

Comparisonsbetweencurrentsolutionmethodandproposedsolutionmethod


Computational experiments10

Computational experiments

Practicalapplication: Solutionmethodapplied

MONDAY

CURRENT

PROPOSED


Computational experiments11

Computational experiments

Practicalapplication: Solutionmethodapplied

TUESDAY

CURRENT

PROPOSED


Computational experiments12

Computational experiments

Practicalapplication: Solutionmethodapplied

WEDNESDAY

CURRENT

PROPOSED


Computational experiments13

Computational experiments

Practicalapplication: Solutionmethodapplied

THURSDAY

CURRENT

PROPOSED


Computational experiments14

Computational experiments

Practicalapplication: Solutionmethodapplied

FRIDAY

CURRENT

PROPOSED


Conclusions

Conclusions

  • Goodsolutionmethod for thePVRP

  • Goodresults for thepractical case:

    • Routeoptimization

    • Reliableprocedure

      • Service levelguaranteed

      • Costcontrol

    • Easyset-up

    • Decisionmaking tool


Future research directions

Future ResearchDirections

  • Testinganotherinsertionmethods (i.e. GENI)

  • Populationdiversitycontrol

  • Apply more mutationoperators

  • Multicriteriaanalisysfor fitness evaluation

  • Automaticand/ordynamiccalibration

    • Meta-AGs

    • AI


Future research directions1

Future ResearchDirections

  • Directaproach for balancing

  • Spatialrouteclustering for eachvehicleduringplanningperiod


Thank you

UNIVERSIDADE FEDERAL DO CEARÁ

PRÓ-REITORIA DE PESQUISA E PÓS-GRADUAÇÃO

PROGRAMA DE MESTRADO EM LOGÍSTICA E PESQUISA OPERACIONAL

THANK YOU!

José Lassance de Castro Silva <[email protected]>

Felipe Pinheiro Bezerra <[email protected]>

CYTEDHAROSA 2012


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