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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.

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A Genetic Algorithm for Period Vehicle Routing Problem with Practical Application

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  1. 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

  2. Outline • MotivatingProblem • ProblemDefinition • SolutionMethodAproach • ComputationalExperiments • Conclusionsand Future ResearchDirections

  3. MotivatingProblem • WholesalerDistributor • Ice cream and ice pops division • Sales team • Marketing mix: • Product • Pricing • Promotion • Placement Practicalapplication:

  4. MotivatingProblem Practicalapplication: • SALES TEAM ROUTINE AT CUSTOMER STORE • Observe visibilityandpromotionelements • Inspectequipments (freezers) • Clean theequipmentsandrearrangetheproductsinsidethem • Remove strangeproducts • Analysesupply, assortmentandprices • Negotiateimprovementsandorders • Placeorder

  5. MotivatingProblem Practicalapplication: Currentsolutionmethod

  6. MotivatingProblem • Advantages: • Out of route serving • Intuitive inclusion of new customers • Sales representative´s familiarity with territory Practicalapplication: Currentsolutionmethod

  7. MotivatingProblem • Drawbacks: • No tour definition • Replanning cost (time) • Learning curve • Unable to handle customer with multiple service frequence demand Practicalapplication: Currentsolutionmethod

  8. MotivatingProblem • Predefinedfrequence a regularity • Routeoptimization • Savetravel time • Increasesalesoportunity • Minimize travelcostsandrisks • Fastandeasyto use • Operationalrestrictions • Team size • Daily workload Practicalapplication: Considerations

  9. 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.

  10. The Periodic Vehicle Routing Problem (PVRP) (BALDACCI et al., 2011) 1 vehicle 30 unitsofcapacity

  11. The Periodic Vehicle Routing Problem (PVRP) • Select a visit schedule for eachcustomer; • Define thecustomersthatshouldbevisitedbyeachvehicleoneachday; • Routethevehicles for eachday. Threesimultaneousdecisions: It´s a generalizationoftheVRP: NP-Hard.

  12. SolutionMethodAproach • Holland (1975) • Metaheuristic • Natural selection • Populationbased • Cromossomes/individuals • Recombinations • Fitness GeneticAlgorithms: Concepts

  13. 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

  14. SolutionMethodAproach • Solutionrepresentation • Fitness function • Populationcontrol • Selectionmethod • Geneticoperators • Use ofhibridization • Stop criteria • Parametersdefinition GeneticAlgorithms: Key points

  15. SolutionMethodAproach • Solutionrepresentation • Grand Tour • No tripdelimiters • Prins (2004), Chu et al. (2004) e Vidal et al. (2012) Proposedgeneticalgorithm: (VIDAL et al. 2012)

  16. SolutionMethodAproach • Individuals evaluation: Split algorithm (PRINS 2004) Proposedgeneticalgorithm: (PRINS, 2004)

  17. SolutionMethodAproach • Original crossover operator Proposed genetic algorithm:

  18. 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

  19. 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

  20. Computational experiments • Fair results • Lowcomputationalcosts Benchmark instancestesting:

  21. 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

  22. Computational experiments Practicalapplication: Solutionmethodapplied

  23. Computational experiments • Adjustments: • Demand = service time • Restrictions = dailyworkload in mimutes • Travel time • Penalties for notusingevery“vehicle” daily Practicalapplication: Solutionmethodapplied

  24. Computational experiments Practicalapplication: Solutionmethodapplied Distance savings over planning period Average daily workload composition per salesman (minutes).

  25. Computational experiments • Initialfindings: • Downtimeawareness • Trade-off betweensavingsandworkloadbalancing • “Howmuch does theworkloadbalancingcost?” Practicalapplication: Solutionmethodapplied

  26. Computational experiments Practicalapplication: Solutionmethodapplied .

  27. Computationalexperiments Practicalapplication: Solutionmethodapplied Comparisonsbetweencurrentsolutionmethodandproposedsolutionmethod

  28. Computational experiments Practicalapplication: Solutionmethodapplied MONDAY CURRENT PROPOSED

  29. Computational experiments Practicalapplication: Solutionmethodapplied TUESDAY CURRENT PROPOSED

  30. Computational experiments Practicalapplication: Solutionmethodapplied WEDNESDAY CURRENT PROPOSED

  31. Computational experiments Practicalapplication: Solutionmethodapplied THURSDAY CURRENT PROPOSED

  32. Computational experiments Practicalapplication: Solutionmethodapplied FRIDAY CURRENT PROPOSED

  33. Conclusions • Goodsolutionmethod for thePVRP • Goodresults for thepractical case: • Routeoptimization • Reliableprocedure • Service levelguaranteed • Costcontrol • Easyset-up • Decisionmaking tool

  34. Future ResearchDirections • Testinganotherinsertionmethods (i.e. GENI) • Populationdiversitycontrol • Apply more mutationoperators • Multicriteriaanalisysfor fitness evaluation • Automaticand/ordynamiccalibration • Meta-AGs • AI

  35. Future ResearchDirections • Directaproach for balancing • Spatialrouteclustering for eachvehicleduringplanningperiod

  36. 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 <lassance@lia.ufc.br> Felipe Pinheiro Bezerra <FELIPE@FORTALI.COM.BR> CYTEDHAROSA 2012

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