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

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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