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  • Object Oriented Modules for Dynamic Vehicle Routing - Research Proposal - Victor Pillac ´ Ecole des Mines de Nantes IRCCyN UMR 6597 Nantes, France Universidad de Los Andes Industrial Engineering Department Bogot´, Colombia a v.pillac63@uniandes.edu.co vpillac@mines-nantes.fr December 9, 2011
  • Outline1 Introduction Dynamic vehicle routing Literature review Motivation2 Dynamic and deterministic routing Parallel adaptive large neighborhood search Application to the TRSP Bi-objective dynamic routing Application to the D-VRPTW3 Dynamic and stochastic routing Multiple scenario approach Application to the D-VRPSD4 Conclusions Contributions Plan for 3rd year V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 2 / 44
  • IntroductionOutline1 Introduction Dynamic vehicle routing Literature review Motivation2 Dynamic and deterministic routing Parallel adaptive large neighborhood search Application to the TRSP Bi-objective dynamic routing Application to the D-VRPTW3 Dynamic and stochastic routing Multiple scenario approach Application to the D-VRPSD4 Conclusions Contributions Plan for 3rd year V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 3 / 44
  • Introduction Dynamic vehicle routingOutline1 Introduction Dynamic vehicle routing Literature review Motivation2 Dynamic and deterministic routing Parallel adaptive large neighborhood search Application to the TRSP Bi-objective dynamic routing Application to the D-VRPTW3 Dynamic and stochastic routing Multiple scenario approach Application to the D-VRPSD4 Conclusions Contributions Plan for 3rd year V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 4 / 44
  • Introduction Dynamic vehicle routingA taxonomy of routing problems Information quality Deterministic Stochastic input input Input known Static and Static and Information beforehand deterministic stochastic evolution Input changes Dynamic and Dynamic and over time deterministic stochastic V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 5 / 44
  • Introduction Dynamic vehicle routingA taxonomy of routing problems Information quality Deterministic Stochastic input input Input known Static and Static and Information beforehand deterministic stochastic evolution Input changes Dynamic and Dynamic and over time deterministic stochastic We focus on problems in which input changes over time New clients Demand realization V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 5 / 44
  • Introduction Dynamic vehicle routingDynamic vehicle routing Environment Vehicle Starts Finishes New customer: is ready serving A serving A X Next: A Next: B Decision Update Decision Update Dispatcher Figure: Timeline of events in dynamic vehicle routing V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 6 / 44
  • Introduction Literature reviewOutline1 Introduction Dynamic vehicle routing Literature review Motivation2 Dynamic and deterministic routing Parallel adaptive large neighborhood search Application to the TRSP Bi-objective dynamic routing Application to the D-VRPTW3 Dynamic and stochastic routing Multiple scenario approach Application to the D-VRPSD4 Conclusions Contributions Plan for 3rd year V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 7 / 44
  • Introduction Literature reviewLiterature review Complete review in [Pillac et al., 2011a] V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 8 / 44
  • Introduction Literature reviewLiterature review Complete review in [Pillac et al., 2011a] Dynamic and deterministic Fast reoptimization [Montemanni et al., 2005, Gambardella et al., 2003] Adaptive memory [Gendreau et al., 1999, Bent and Van Hentenryck, 2004, Benyahia and Potvin, 1998] V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 8 / 44
  • Introduction Literature reviewLiterature review Complete review in [Pillac et al., 2011a] Dynamic and deterministic Fast reoptimization [Montemanni et al., 2005, Gambardella et al., 2003] Adaptive memory [Gendreau et al., 1999, Bent and Van Hentenryck, 2004, Benyahia and Potvin, 1998] Dynamic and stochastic Stochastic modeling [Godfrey and Powell, 2002, Powell and Topaloglu, 2005, Simao et al., 2009, Novoa and Storer, 2009] Sampling [Van Hentenryck and Bent, 2006] V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 8 / 44
  • Introduction MotivationOutline1 Introduction Dynamic vehicle routing Literature review Motivation2 Dynamic and deterministic routing Parallel adaptive large neighborhood search Application to the TRSP Bi-objective dynamic routing Application to the D-VRPTW3 Dynamic and stochastic routing Multiple scenario approach Application to the D-VRPSD4 Conclusions Contributions Plan for 3rd year V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 9 / 44
  • Introduction MotivationMotivation Study the different aspects of dynamic routing Differences with static routing Degrees of dynamism Algorithm performance evaluation Important algorithm features Develop software libraries Extensible Flexible Apply findings to a case study Routing of a crew of technicians V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 10 / 44
  • Dynamic and deterministic routingOutline1 Introduction Dynamic vehicle routing Literature review Motivation2 Dynamic and deterministic routing Parallel adaptive large neighborhood search Application to the TRSP Bi-objective dynamic routing Application to the D-VRPTW3 Dynamic and stochastic routing Multiple scenario approach Application to the D-VRPSD4 Conclusions Contributions Plan for 3rd year V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 11 / 44
  • Dynamic and deterministic routingDynamic and deterministic routing Consider dynamic changes in input No information is available on the dynamically revealed data Optimization approaches Use simple insertion heuristics Split the planning horizon in decision epochs Perform an optimization each time a change occurs Use an adaptive memory to store previous good solutions characteristics V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 12 / 44
  • Dynamic and deterministic routing Parallel adaptive large neighborhood searchOutline1 Introduction Dynamic vehicle routing Literature review Motivation2 Dynamic and deterministic routing Parallel adaptive large neighborhood search Application to the TRSP Bi-objective dynamic routing Application to the D-VRPTW3 Dynamic and stochastic routing Multiple scenario approach Application to the D-VRPSD4 Conclusions Contributions Plan for 3rd year V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 13 / 44
  • Dynamic and deterministic routing Parallel adaptive large neighborhood searchParallel Adaptive Large Neighborhood Search (pALNS) Extension of ALNS [Pisinger and Ropke, 2007] Make use of multi-core architecture Maintain a pool of promising solutions Limit synchronization between threads for performancepALNS AlgorithmInput: Π0 an initial solutionOutput: The best solution found by the algorithm 1: Initialize the promising solution pool Ω ← {Π0 } 2: for N iterations do 3: Select subset Ωt of solutions 4: parallel forall Π in Ωt do 5: In a separate thread, perform n ALNS iterations starting with Π 6: Report the resulting solution to the main thread 7: end forall 8: Update the pool of promising solutions Ω 9: end for10: return The best solution from Ω V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 14 / 44
  • Dynamic and deterministic routing Parallel adaptive large neighborhood searchComputational results Tested on Solomon benchmark [Solomon, 1987] Factor 3 speedups Reduced running time variance Gap increased by 0.18% q q q q q q 200 0.06 q q q q q q q q q q q q q q q q q q q q q q q 150 0.04 qGAP Time q q q q q q 100 0.02 q q q q q q q q q 0.00 50 1 2 4 8 1 2 4 8 Threads Threads V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 15 / 44
  • Dynamic and deterministic routing Application to the TRSPOutline1 Introduction Dynamic vehicle routing Literature review Motivation2 Dynamic and deterministic routing Parallel adaptive large neighborhood search Application to the TRSP Bi-objective dynamic routing Application to the D-VRPTW3 Dynamic and stochastic routing Multiple scenario approach Application to the D-VRPSD4 Conclusions Contributions Plan for 3rd year V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 16 / 44
  • Dynamic and deterministic routing Application to the TRSPTechnician Routing and Scheduling Problem (TRSP) Has: 6 Has: 1 5 2 3 Pickup 4 Set of requests Crew of technicians Location Starting/ending location Required skills, tools, spare (home) parts Set of skills, initial tools, Time window and service spare parts time Working day length Main depot Technicians can pickup tools and spare parts Unlimited tools and spare parts V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 17 / 44
  • Dynamic and deterministic routing Application to the TRSPStatic TRSPGoalHave a reference approach for the dynamic version Proposed approach Adaptive Large Neighborhood Search (ALNS) Set covering using routes generated throughout ALNS Presented in [Pillac et al., 2011c] Computational experiments Solomon benchmark (VRPTW) 38/38 Optimal solutions 14/18 Best known solution, 9 improved Randomly generated TRSP instances based on Solomon instances V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 18 / 44
  • Dynamic and deterministic routing Application to the TRSPDynamic TRSP Generated release date for a proportion of requests Compare to a Best Insertion (BI) heuristicResults ALNS BI DOD VI (%) R VI (%) R 10 4.52 0.08 16.58 0.33 50 7.10 0.42 44.81 1.75 90 9.50 1.58 52.94 2.83 Average 7.04 0.69 38.11 1.64VI: Value of information, R: average number of rejected requests V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 19 / 44
  • Dynamic and deterministic routing Bi-objective dynamic routingOutline1 Introduction Dynamic vehicle routing Literature review Motivation2 Dynamic and deterministic routing Parallel adaptive large neighborhood search Application to the TRSP Bi-objective dynamic routing Application to the D-VRPTW3 Dynamic and stochastic routing Multiple scenario approach Application to the D-VRPSD4 Conclusions Contributions Plan for 3rd year V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 20 / 44
  • Dynamic and deterministic routing Bi-objective dynamic routingBi-objective dynamic routing Static routing generally aims at designing a set of routes For a given day (operational) For a longer horizon (tactical,strategic) Minimizing cost/distance/fleet size Dynamic routing introduces new dimensions Changes in the routing while the vehicle is en-route Changes in the driver/request assignments Communication with vehicles New problematic How to ensure cost efficiency? How to ensure stability in the routing? How to optimize both objectives in limited time? What is the trade-off between the two objectives? V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 21 / 44
  • Dynamic and deterministic routing Bi-objective dynamic routingMeasuring stability D-VRP case: a new request r arrives at time t Let: Ω the set of all solutions Πt−1 the previous solution Πt a solution including r How stable is Πt with respect to Πt−1 ? V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 22 / 44
  • Dynamic and deterministic routing Bi-objective dynamic routingMeasuring stability D-VRP case: a new request r arrives at time t Let: Ω the set of all solutions Πt−1 the previous solution Πt a solution including r How stable is Πt with respect to Πt−1 ? Define a metric δ : Ω × Ω → R How to define δ? V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 22 / 44
  • Dynamic and deterministic routing Bi-objective dynamic routingMeasuring stability D-VRP case: a new request r arrives at time t Let: Ω the set of all solutions Πt−1 the previous solution Πt a solution including r How stable is Πt with respect to Πt−1 ? Define a metric δ : Ω × Ω → R How to define δ? Hamming distance Number of changed assignments Edit (Levenshtein) distance Insertions Removals Substitutions V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 22 / 44
  • Dynamic and deterministic routing Bi-objective dynamic routingBi-objective Parallel ALNS Make use of multi-core architecture Focus on the distance during ALNS iterations Store non-dominated solutionspBiALNS AlgorithmInput: Π0 an initial solutionOutput: The non-dominated solutions found by the algorithm 1: Initialize the non-dominated solutions Ω ← {Π0 } 2: for N iterations do 3: Select subset Ωt of non-dominated solutions 4: parallel forall Π in Ωt do 5: In a separate thread, perform n ALNS iterations starting with Π 6: Report the non-dominated solutions to the main thread 7: end forall 8: Update the non-dominated solutions Ω 9: end for10: return The non-dominated solutions Ω V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 23 / 44
  • Dynamic and deterministic routing Application to the D-VRPTWOutline1 Introduction Dynamic vehicle routing Literature review Motivation2 Dynamic and deterministic routing Parallel adaptive large neighborhood search Application to the TRSP Bi-objective dynamic routing Application to the D-VRPTW3 Dynamic and stochastic routing Multiple scenario approach Application to the D-VRPSD4 Conclusions Contributions Plan for 3rd year V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 24 / 44
  • Dynamic and deterministic routing Application to the D-VRPTWBi-objective Dynamic VRP-TW Simulation of a dynamic setting Instance C101 90/100 revealed requests Part of the routes is already executed A new request is revealed pBiALNS run for 25,000 iterations 100 90 80 70 60 Pareto Edit distance 50 ALNS solutions 40 30 20 10 0 0% 2% 4% 6% 8% 10% 12% 14% 16% 18% 20% Gap V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 25 / 44
  • Dynamic and deterministic routing Application to the D-VRPTWBi-objective Dynamic VRP-TW Simulation of a dynamic setting Instance C101 90/100 revealed requests Part of the routes is already executed A new request is revealed pBiALNS run for 25,000 iterations 70 60 50 Pareto 40 Edit distance ALNS 30 solutions 20 10 0 0% 1% 2% 3% 4% Gap V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 25 / 44
  • Dynamic and stochastic routingOutline1 Introduction Dynamic vehicle routing Literature review Motivation2 Dynamic and deterministic routing Parallel adaptive large neighborhood search Application to the TRSP Bi-objective dynamic routing Application to the D-VRPTW3 Dynamic and stochastic routing Multiple scenario approach Application to the D-VRPSD4 Conclusions Contributions Plan for 3rd year V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 26 / 44
  • Dynamic and stochastic routingDynamic and stochastic routing Consider dynamic changes in input Stochastic information is available on the dynamically revealed data Random variables of known distribution Historical data Optimization approaches Ignore stochastic information Model stochastic information Use sampling to capture uncertainty V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 27 / 44
  • Dynamic and stochastic routing Multiple scenario approachOutline1 Introduction Dynamic vehicle routing Literature review Motivation2 Dynamic and deterministic routing Parallel adaptive large neighborhood search Application to the TRSP Bi-objective dynamic routing Application to the D-VRPTW3 Dynamic and stochastic routing Multiple scenario approach Application to the D-VRPSD4 Conclusions Contributions Plan for 3rd year V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 28 / 44
  • Dynamic and stochastic routing Multiple scenario approachMultiple scenario approach Sampling based approach Framework for online optimization [Van Hentenryck and Bent, 2006] Runs throughout the day Scenario pool Scenario pool Realizations of the random variables Decision process Next client Which client next? Aggregate scenario knowledge V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 29 / 44
  • Dynamic and stochastic routing Multiple scenario approachMSA : ScenariosScenario generation Example 1 Start with known data D C E A BGoalCapture uncertainty through sampling V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 30 / 44
  • Dynamic and stochastic routing Multiple scenario approachMSA : ScenariosScenario generation Example 1 Start with known data 2 Sample random variable D C distributions 3 Solve resulting optimization X E problem A Z B YGoalCapture uncertainty through sampling V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 30 / 44
  • Dynamic and stochastic routing Multiple scenario approachMSA : ScenariosScenario generation Example 1 Start with known data 2 Sample random variable D C distributions 3 Solve resulting optimization X E problem A Z 4 Remove sample data from B Y solutionGoalCapture uncertainty through sampling V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 30 / 44
  • Dynamic and stochastic routing Multiple scenario approachMSA : DecisionsDecision process Decide which request to visit next Aggregate information from all scenarios Fast process V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 31 / 44
  • Dynamic and stochastic routing Multiple scenario approachMSA : DecisionsDecision process Example Decide which request to visit Consensus next Select the request that Aggregate information from all appear first in most scenarios scenarios Fast process S1 0 4 1 6 5 0 ... S2 0 4 1 3 0 ... S3 0 4 1 2 3 6 0 ... S4 0 4 1 2 3 6 5 0 V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 31 / 44
  • Dynamic and stochastic routing Multiple scenario approachMSA : DecisionsDecision process Example Decide which request to visit Consensus next Select the request that Aggregate information from all appear first in most scenarios scenarios Fast process S1 0 4 1 6 5 0 ... S2 0 4 1 3 0 ... S3 0 4 1 2 3 6 0 ... S4 0 4 1 2 3 6 5 0GoalUse scenarios to take a non-myopic decision V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 31 / 44
  • Dynamic and stochastic routing Multiple scenario approachjMSA Java implementation of the MSA Highlights Event-driven Flexible Extensible Described in [Pillac et al., 2011b] MSA procedure Event Handler Scenario queue manager pool Events Handlers Kernel Components Scenario Callback Scenario Scenario Scenario … Problem layer generator optimizer updater V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 32 / 44
  • Dynamic and stochastic routing Application to the D-VRPSDOutline1 Introduction Dynamic vehicle routing Literature review Motivation2 Dynamic and deterministic routing Parallel adaptive large neighborhood search Application to the TRSP Bi-objective dynamic routing Application to the D-VRPTW3 Dynamic and stochastic routing Multiple scenario approach Application to the D-VRPSD4 Conclusions Contributions Plan for 3rd year V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 33 / 44
  • Dynamic and stochastic routing Application to the D-VRPSDDynamic VRP with Stochastic Demands Extension of the Vehicle Routing Problem Stochastic Demands (known distribution law) Traditional approaches Two-stage approach Robust a-priori routing Recourse actions in case of failure Assumes vehicles cannot be rerouted Why a dynamic approach? Increasing availability of low-cost positioning systems Real-time communication with vehicles Allows further optimization V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 34 / 44
  • Dynamic and stochastic routing Application to the D-VRPSDComputational experiments 30 Instances from Novoa (2005) 30, 40 and 60 customers Randomly distributed in 1x1 square Uniform discrete demand distribution 2 vehicle capacities V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 35 / 44
  • Dynamic and stochastic routing Application to the D-VRPSDComputational experiments 30 Instances from Novoa (2005) 30, 40 and 60 customers Randomly distributed in 1x1 square Uniform discrete demand distribution 2 vehicle capacitiesValue of information Algorithm min. max. avg. [Secomandi, 2001] 11.1% 19.6% 13.6% [Novoa and Storer, 2009]-1 3.5% 12.3% 5.8% [Novoa and Storer, 2009]-2 2.8% 10.7% 4.8% jMSA 0.9% 6.3% 3.3% V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 35 / 44
  • Dynamic and stochastic routing Application to the D-VRPSDComputational experiments 30 Instances from Novoa (2005) 30, 40 and 60 customers Randomly distributed in 1x1 square Uniform discrete demand distribution 2 vehicle capacitiesValue of information Algorithm min. max. avg. [Secomandi, 2001] 11.1% 19.6% 13.6% [Novoa and Storer, 2009]-1 3.5% 12.3% 5.8% [Novoa and Storer, 2009]-2 2.8% 10.7% 4.8% jMSA 0.9% 6.3% 3.3% Conclusions Better value of information Faster decisions (ms vs min) V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 35 / 44
  • ConclusionsOutline1 Introduction Dynamic vehicle routing Literature review Motivation2 Dynamic and deterministic routing Parallel adaptive large neighborhood search Application to the TRSP Bi-objective dynamic routing Application to the D-VRPTW3 Dynamic and stochastic routing Multiple scenario approach Application to the D-VRPSD4 Conclusions Contributions Plan for 3rd year V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 36 / 44
  • Conclusions ContributionsOutline1 Introduction Dynamic vehicle routing Literature review Motivation2 Dynamic and deterministic routing Parallel adaptive large neighborhood search Application to the TRSP Bi-objective dynamic routing Application to the D-VRPTW3 Dynamic and stochastic routing Multiple scenario approach Application to the D-VRPSD4 Conclusions Contributions Plan for 3rd year V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 37 / 44
  • Conclusions ContributionsContributions IWorking papers Pillac, V., Gendreau, M., Guret, C., Medaglia, A. L. (2011), A review of dynamic vehicle routing problems, European Journal of Operational Research, Under review Pillac, V., Guret, C., Medaglia, A. L. (2011), An event-driven optimization framework for dynamic vehicle routing, Decision Support Systems, Under review Pillac, V., Guret, C., Medaglia, A. L. (2011), On the Technician Routing and Scheduling Problem, Optimization Letters, Under reviewTechnical reports Pillac, V., Gendreau, M., Guret, C., Medaglia, A. L. (2011), A review of dynamic vehicle routing problems, CIRRELT Research Paper, CIRRELT-2011-62 Pillac, V., Guret, C., Medaglia, A. L. (2011), An event-driven optimization framework for dynamic vehicle routing, Technical Report 11/2/AUTO, Ecole des Mines de Nantes, France Pillac, V., Guret, C., Medaglia, A. L. (2010), Dynamic Vehicle Routing Problems: State of the art and Prospects, Technical Report 10/4/AUTO, Ecole des Mines de Nantes, France V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 38 / 44
  • Conclusions ContributionsContributions IIConference proceedings Pillac, V., Guret, C., Medaglia, A. L. (2011), On the Technician Routing and Scheduling Problem, Proceedings of the 9th Metaheuristics International Conference (MIC 2011), 675-678, Udine (Italy) Pillac, V., Guret, C., Medaglia, A. L. (2011), A dynamic approach for the vehicle routing problem with stochastic demands, ROADEF 2011, St Etienne (France) Pillac, V., Guret, C., Medaglia, A. L. (2010), Solving the Vehicle Routing Problem with Stochastic Demands with a Multiple Scenario Approach, ALIO-INFORMS 2010, Buenos Aires (Argentina)Software libraries Pillac, V., Guret, C., Medaglia, A. L., VroomModeling: A general purpose modeling library for vehicle routing problems. Pillac, V., Guret, C., Medaglia, A. L., VroomHeuristics: A set of general heuristics for vehicle routing problems. Pillac, V., Guret, C., Medaglia, A. L., jMSA: An event-driven optimization framework for dynamic vehicle routing. V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 39 / 44
  • Conclusions Plan for 3rd yearOutline1 Introduction Dynamic vehicle routing Literature review Motivation2 Dynamic and deterministic routing Parallel adaptive large neighborhood search Application to the TRSP Bi-objective dynamic routing Application to the D-VRPTW3 Dynamic and stochastic routing Multiple scenario approach Application to the D-VRPSD4 Conclusions Contributions Plan for 3rd year V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 40 / 44
  • Conclusions Plan for 3rd yearPlan for 3rd yearResearch 1 Bi-objective approach for dynamic routingJournal papers 1 Parallel ALNS and bi-objective dynamic routingConferences 1 ROADEF’12 2 ODYSSEUS’12 V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 41 / 44
  • Conclusions Plan for 3rd yearQuestions & Answers Thank you for your attention V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 42 / 44
  • ReferencesReferences I Bent, R. and Van Hentenryck, P. (2004). Scenario-based planning for partially dynamic vehicle routing with stochastic customers. Operations Research, 52(6):977–987. Benyahia, I. and Potvin, J. Y. (1998). Decision support for vehicle dispatching using genetic programming. IEEE Transactions on Systems Man and Cybernetics Part A - Systems and Humans, 28(3):306–314. Gambardella, L., Rizzoli, A., Oliverio, F., Casagrande, N., Donati, A., Montemanni, R., and Lucibello, E. (2003). Ant colony optimization for vehicle routing in advanced logistics systems. In Proceedings of the International Workshop on Modelling and Applied Simulation (MAS 2003), pages 3–9. Gendreau, M., Guertin, F., Potvin, J.-Y., and Taillard, E. (1999). Parallel tabu search for real-time vehicle routing and dispatching. Transportation Science, 33(4):381–390. Godfrey, G. and Powell, W. B. (2002). An adaptive dynamic programming algorithm for dynamic fleet management, I: Single period travel times. Transportation Science, 36(1):21–39. Montemanni, R., Gambardella, L. M., Rizzoli, A. E., and Donati, A. V. (2005). Ant colony system for a dynamic vehicle routing problem. Journal of Combinatorial Optimization, 10(4):327–343. Novoa, C. and Storer, R. (2009). An approximate dynamic programming approach for the vehicle routing problem with stochastic demands. European Journal of Operational Research, 196(2):509–515. Pillac, V., Gendreau, M., Gu´ret, C., and Medaglia, A. L. (2011a). A review of dynamic vehicle routing problems. Technical e report, CIRRELT. CIRRELT-2011-62. Pillac, V., Gu´ret, C., and Medaglia, A. L. (2011b). An event-driven optimization framework for dynamic vehicle routing. e ´ Technical report, Ecole des Mines de Nantes, France. Report 11/2/AUTO. Pillac, V., Gu´ret, C., and Medaglia, A. L. (2011c). On the technician routing and scheduling problem. Optimization e Letters, Under review. V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 43 / 44
  • ReferencesReferences II Pisinger, D. and Ropke, S. (2007). A general heuristic for vehicle routing problems. Computers & Operations Research, 34(8):2403–2435. Powell, W. B. and Topaloglu, H. (2005). Fleet management. In Wallace, S. and Ziemba, W., editors, Applications of Stochastic Programming, volume 5 of MPS-SIAM series on Optimization, chapter 12, pages 185–215. SIAM. Secomandi, N. (2001). A rollout policy for the vehicle routing problem with stochastic demands. Operations Research, 49(5):796–802. Simao, H., Day, J., George, A., Gifford, T., Nienow, J., and Powell, W. B. (2009). An approximate dynamic programming algorithm for large-scale fleet management: A case application. Transportation Science, 43(2):178–197. Solomon, M. M. (1987). Algorithms for the vehicle-routing and scheduling problems with time window constraints. Operations Research, 35(2):254–265. Van Hentenryck, P. and Bent, R. (2006). Online stochastic combinatorial optimization. MIT Press. V. Pillac (EMN/Uniandes) Research Proposal 2011/12/09 44 / 44