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Part of the book series: Operations Research/Computer Science Interfaces ((ORCS,volume 43))

Summary

Inter-tour constraints are constraints in a vehicle-routing problem (VRP) on globally limited resources that different vehicles compete for.% Real-world examples are a limited number of ‘‘long’’ tours, where long is defined with respect to the traveled distance, the number of stops, the arrival time at the depot etc. % Moreover, a restricted number of docking stations or limited processing capacities for incoming goods at the destination depot can be modeled by means of inter-tour resource constraints.% In this chapter, we introduce a generic model for VRPs with inter-tour constraints based on the giant-tour representation and resource-constrained paths.% Furthermore, solving the model by efficient local search techniques is addressed: % Tailored preprocessing procedures and feasibility tests are combined into local-search algorithms, that are attractive from a worst-case point of view and are superior to traditional search techniques in the average case. % In the end, the chapter provides results for some new types of studies where VRPs with time-varying processing capacities are analyzed.

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References

  1. E. Aarts and J. Korst.Simulated Annealing and Boltzmann Machines. Wiley, Chichester, 1989.

    Google Scholar 

  2. P. Avella, M. Boccia, and A. Sforza. Resource constrained shortest path problems in path planning for fleet management.Journal of Mathematical Modelling and Algorithms, 3:1–17, 2004.

    Article  Google Scholar 

  3. O. Bräysy and M. Gendreau. Vehicle routing with time windows, Part I: Route construction and local search algorithms.Transportation Science, 39:104–118, 2005.

    Article  Google Scholar 

  4. O. Bräysy and M. Gendreau. Vehicle routing with time windows, Part II: Metaheuristics.Transportation Science, 39:119–139, 2005.

    Article  Google Scholar 

  5. N. Christofides and S. Eilon. An algorithm for the vehicle-dispatching problem.Operational Research Quarterly, 20:309–318, 1969.

    Google Scholar 

  6. N. Christofides and S. Eilon. Algorithms for large-scale travelling salesman problems.Operational Research Quarterly, 23:511–518, 1972.

    Google Scholar 

  7. G. Desaulniers, J. Desrosiers, I. Ioachim, M.M. Solomon, F. Soumis, and D. Villeneuve. A unified framework for deterministic time constrained vehicle routing and crew scheduling problems, Chapter 3 inFleet Management and Logistics, T. Crainic and G. Laporte, eds., Kluwer Academic Publisher, Boston, 1998.

    Google Scholar 

  8. B. Funke, T. Grünert, and S. Irnich. Local search for vehicle routing and scheduling problems: Review and conceptual integration.Journal of Heuristics, 11:267–306, 2005.

    Article  Google Scholar 

  9. P. Hansen and N. Mladenovi´c. Variable neighborhood search: Principles and applications.European Journal of Operational Research, 130:449–467, 2001.

    Article  Google Scholar 

  10. S. Irnich. Resource extension functions: Properties, inversion, and generalization to segments. Technical Report 2006-01, Deutsche Post Endowed Chair of Optimization of Distribution Networks, RWTH Aachen University, Aachen, Germany, 2006. Available at www.dpor.rwth-aachen.de, forthcoming in OR Spectrum.

    Google Scholar 

  11. S. Irnich. A unified modeling and solution framework for vehicle routing and local search-based metaheuristics. Technical Report 2006-02, Deutsche Post Endowed Chair of Optimization of Distribution Networks, RWTH Aachen University, Aachen, Germany, 2006. Available at www.dpor.rwth-aachen.de, accepted with minor modifications for publication in INFORMS Journal on Computing.

    Google Scholar 

  12. S. Irnich and G. Desaulniers. Shortest path problems with resource constraints, Chapter 2 inColumn Generation, G. Desaulniers, J. Desrosiers, and M.M. Solomon, eds., Springer, 2005.

    Google Scholar 

  13. S. Irnich, B. Funke, and T. Grünert. Sequential search and its application to vehicle-routing problems.Computers & Operations Research, 33:2405–2429, 2006.

    Article  Google Scholar 

  14. B.W. Kernighan and S. Lin. An efficient heuristic procedure for partitioning graphs.Bell Syst. Tech. J., 49:291–307, 1970.

    Google Scholar 

  15. G.A.P. Kindervater and M.W.P. Savelsbergh. Vehicle routing: Handling edge exchanges, Chapter 10 inLocal Search in Combinatorial Optimization, E. Aarts and J. Lenstra, eds., Wiley, Chichester, 1997.

    Google Scholar 

  16. S. Lin and B.W. Kernighan. An effective heuristic algorithm for the traveling-salesman problem.Operations Research, 21:498–516, 1973.

    Article  Google Scholar 

  17. O. Martin, S.W. Otto, and E.W. Felten. Large-step Markov chains for the TSP incorporating local search heuristics.Operations Research Letters, 11:219–224, 1992.

    Article  Google Scholar 

  18. N. Mladenovi´c and P. Hansen. Variable neighborhood search.Computers & Operations Research, 24:1097–1100, 1997.

    Article  Google Scholar 

  19. D. Pisinger and S. Røpke. A general heuristic for vehicle routing problems.Computers & Operations Research, 34:2403–2435, 2007.

    Article  Google Scholar 

  20. J.-Y. Potvin, G. Lapalme, and J.-M. Rousseau. A generalized k-opt exchange procedure for the MTSP.Information Systems and Operations Research, 27:474–481, 1989.

    Google Scholar 

  21. S. Røpke and D. Pisinger. An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows.Transportation Science, 40:455–472, 2006.

    Article  Google Scholar 

  22. M.W.P. Savelsbergh. Local search for routing problems with time windows. inAlgorithms and Software for Optimization, Part I, C.L. Monma, ed., Baltzer, Basel, Volume 4:285–305, 1986.

    Google Scholar 

  23. M.W.P. Savelsbergh. An efficient implementation of local search algorithms for constrained routing problems.European Journal of Operational Research, 47:75–85, 1990.

    Article  Google Scholar 

  24. P. Shaw. Using constraint programming and local search methods to solve vehicle routing problems.Lecture Notes in Computer Science, Volume 1520:417–431, 1998.

    Article  Google Scholar 

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Correspondence to Christoph Hempsch .

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Hempsch, C., Irnich, S. (2008). Vehicle Routing Problems with Inter-Tour Resource Constraints. In: Golden, B., Raghavan, S., Wasil, E. (eds) The Vehicle Routing Problem: Latest Advances and New Challenges. Operations Research/Computer Science Interfaces, vol 43. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-77778-8_19

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