A Two-Stage Hybrid Local Search for the Vehicle Routing Problem with Time Windows

Published Online:https://doi.org/10.1287/trsc.1030.0049

References

  • Berger J., Barkaoui M., Bräysy O. A parallel hybrid genetic algorithm for the vehicle routing problem with time windows. (2001) (Defense Research Establishment Valcartier, Val Belair, Canada) . Working paperGoogle Scholar
  • Bräysy O. Five local search algorithms for the vehicle routing problem with time windows. (2001a) (Department of Optimization, Norway) . Working paper, SINTEF Applied MathematicsGoogle Scholar
  • Bräysy O. Local search and variable neighborhood search algorithms for the VRPTW. (2001b) (Universitas Wasaensis, Vaasa, Oslo, Finland) . Acta Wasaensia 87, Mathematics 8, Operational ResearchGoogle Scholar
  • Bräysy O. A reactive variable neighborhood search for the vehicle-routing problem with time windows. INFORMS J. Comput. (2003) 15(4):347–368LinkGoogle Scholar
  • Chiang W., Russell R. Simulated annealing metaheuristics for the vehicle routing problem with time windows. Ann. Oper. Res. (1996) 63:3–27CrossrefGoogle Scholar
  • Chiang W., Russell R. A reactive tabu search metaheuristic for the vehicle routing problem with time windows. INFORMS J. Comput. (1997) 9:417–430LinkGoogle Scholar
  • Christofides N., Beasley J. The period routing problem. Networks (1984) 14:237–246CrossrefGoogle Scholar
  • Cordeau J., Laporte G., Mercier A. A unified tabu search heuristic for vehicle routing problems with time windows. J. Oper. Res. Soc. (2001) 52:928–936CrossrefGoogle Scholar
  • Czech Z., Czarnas P. A parallel simulated annealing for the vehicle routing problem with time windows. Proc. 10th Euromicro Workshop Parallel, Distributed Network-based Processing (2002) (Canary Islands, Spain)376–383Google Scholar
  • Davis L.Handbook of Genetic Algorithms (1991) (Van Nostrand Reinhold, New York) Google Scholar
  • De Backer B., Furnon V., Shaw P., Kilby P., Prosser P. Solving vehicle routing problems using constraint programming and metaheuristics. J. Heuristics (2000) 6:501–523CrossrefGoogle Scholar
  • Desrochers M., Desrosiers J., Solomon M. A new optimization algorithm for the vehicle routing problem with time windows. Oper. Res. (1992) 40:342–354LinkGoogle Scholar
  • Fisher M., Joernsten K., Madsen O. Vehicle routing with time windows: Two optimization algorithms. Oper. Res. (1997) 45(3):488–492LinkGoogle Scholar
  • Gambardella L., Taillard E., Agazzi G., Corne D., Dorigo M., Glover F. MACS-VRPTW: A multiple ant colony system for vehicle routing problems with time windows. New Ideas in Optimization (1999) (McGraw-Hill, London, U.K.) 63–76Google Scholar
  • Gehring H., Homberger J. A parallel hybrid evolutionary metaheuristic for the vehicle routing problem with time windows. Proc. EUROGEN99—Short Course on Evolutionary Algorithms Engrg. Comput. Sci. (1999) (University of Jyvaskyla, Jyväskylä, Finland) 57–64Reports of the Department of Mathematical Information Technology Series A, Collections, A2/1999Google Scholar
  • Gehring H., Homberger J. A parallel two-phase metaheuristic for routing problems with time windows. Asia-Pacific J. Oper. Res. (2001) 18:35–47Google Scholar
  • Gendreau M., Laporte G., Potvin J., Aarts E., Lenstra J. Vehicle routing: Modern heuristics. Local Search in Combinatorial Optimization (1997) (John Wiley & Sons Ltd., New York) 311–336Ch. 9Google Scholar
  • Glover F. Tabu search. ORSA J. Comput. (1989) 1:190–206LinkGoogle Scholar
  • Hansen P., Mladenovic N., Voss S., Martello S., Osman I. H., Roucairol C. An introduction to variable neighborhood search. Meta-heuristics, Advances and Trends in Local Search Paradigms for Optimization (1998) (Kluwer Academic Publishers, New York) 433–458Google Scholar
  • Harvey W., Ginsberg M. Limited discrepancy search. (1995) Proc. 14th Internat. Joint Conf. Artificial Intelligence(Montreal, Canada)Google Scholar
  • Homberger J., Gehring H. Two evolutionary metaheuristics for the vehicle routing problem with time windows. INFOR (1999) 37:297–318Google Scholar
  • Homberger W.Verteilt-Parallele Metaheuristiken zur Tourenplanung (2000) (Gaber, Wiesbaden, Germany) Google Scholar
  • Ibaraki T., Kubo M., Masuda T., Uno T., Yagiura M. Effective local search algorithms for the vehicle routing problem with general time windows. (2001) Proc. 4th Metaheuristics Internat. Conf.(Porto, Portugal)Google Scholar
  • Ioannou G., Kritikos M., Prastacos G. A greedy look-ahead heuristic for the vehicle routing problem with time windows. J. Oper. Res. Soc. (2001) 52:523–537CrossrefGoogle Scholar
  • Johnson D., Aragon C., McGeoch L., Schevon C. Optimization by simulated annealing: An experimental evaluation; Part II, graph coloring and number partitioning. Oper. Res. (1991) 39(3):378–406LinkGoogle Scholar
  • Kindervater G., Savelsbergh M., Aarts E., Lenstra J. Vehicle routing: Handling edge exchanges. Local Search in Combinatorial Optimization (1997) (John Wiley & Sons Ltd., New York) 337–360Ch. 10Google Scholar
  • Kirkpatrick S., Gelatt C., Vecchi M. Optimization by simulated annealing. Science (1983) 220:671–680CrossrefGoogle Scholar
  • Kohl N., Desrosiers J., Madsen O., Solomon M., Soumis F. 2-path cuts for the vehicle routing problem with time windows. Transportation Sci. (1999) 33:101–116LinkGoogle Scholar
  • Lenstra J., Rinnooy Kan A. H. G. Complexity of vehicle routing and scheduling problems. Networks (1981) 11:221–227CrossrefGoogle Scholar
  • Or I. Traveling salesman-type combinatorial problems and their relation to the logistics of blood banking. (1976) (Department of Industrial Engineering and Management Science, Northwestern University, Evanston, IL) . Ph.D. thesisGoogle Scholar
  • Osman I. Metastrategy simulated annealing and tabu search algorithms for the vehicle routing problem. Ann. Oper. Res. (1993) 40(1):421–452CrossrefGoogle Scholar
  • Potvin J., Begio S. The vehicle routing problem with time windows—Part II: Genetic search. INFORMS J. Comput. (1996) 8:165–172LinkGoogle Scholar
  • Potvin J., Rousseau J. An exchange heuristic for routing problems with time windows. J. Oper. Res. Soc. (1995) 46:1433–1446CrossrefGoogle Scholar
  • Rochat Y., Taillard E. Probabilistic diversification and intensification in local search for vehicle routing. J. Heuristics (1995) 1:147–167CrossrefGoogle Scholar
  • Rousseau L., Gendreau M., Pesant G. Using constraint-based operators to solve the vehicle routing problem with time windows. J. Heuristics (2002) 8:43–58CrossrefGoogle Scholar
  • Schrimpf G., Schneider J., Stamm-Wilbrandt H., Dueck G. Record breaking optimization results using the ruin and recreate principle. J. Comput. Phys. (2000) 159:139–171CrossrefGoogle Scholar
  • Shaw P. Using constraint programming and local search methods to solve vehicle routing problems. Proc. Principles Practice Constraint Programming (1998) Pisa, Italy:417–431Google Scholar
  • Solomon M. Algorithms for the vehicle routing and scheduling problems with time window constraints. Oper. Res. (1987) 35(2):254–265LinkGoogle Scholar
  • Taillard E., Badeau P., Gendreau M., Geurtin F., Potvin J. A tabu search heuristic for the vehicle routing problem with soft time windows. Transportation Sci. (1997) 31:170–186LinkGoogle Scholar
  • Thangiah S., Osman I., Sun T. Hybrid genetic algorithms, simulated annealing and tabu search methods for vehicle routing problems with time windows. (1994) (University of Kent, Canterbury, U.K.) . Technical Report UKC/OR94/4, Institute of Mathematics & StatisticsGoogle Scholar
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