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A Fast Reoptimization Approach for the Dynamic Technician Routing and Scheduling Problem

  • V. Pillac
  • C. GuéretEmail author
  • A. L. Medaglia
Chapter
Part of the Operations Research/Computer Science Interfaces Series book series (ORCS, volume 62)

Abstract

The Technician Routing and Scheduling Problem (TRSP) consists in routing staff to serve requests for service, taking into account time windows, skills, tools, and spare parts. Typical applications include maintenance operations and staff routing in telecoms, public utilities, and in the health care industry. In this paper we tackle the Dynamic TRSP (D-TRSP) in which new requests appear over time. We propose a fast reoptimization approach based on a parallel Adaptive Large Neighborhood Search (RpALNS) able to achieve state-of-the-art results on the Dynamic Vehicle Routing Problem with Time Windows. In addition, we solve a set of randomly generated D-TRSP instances and discuss the potential gains with respect to a heuristic modeling a human dispatcher solution.

Keywords

Dynamic Vehicle Routing Technician Routing and Scheduling Parallel Adaptive Large Neighborhood Search 

Notes

Acknowledgements

Financial support for this work was provided by the CPER Vallée du Libre (Contrat de Projet Etat Region, France); and the CEIBA (Centro de Estudios Interdisciplinarios Básicos y Aplicados en Complejidad, Colombia). This support is gratefully acknowledged. NICTA is funded by the Australian Government as represented by the Department of Broadband, Communications and the Digital Economy and the Australian Research Council through the ICT Centre of Excellence program.

References

  1. 1.
    A. Attanasio, J.F. Cordeau, G. Ghiani, G. Laporte, Parallel tabu search heuristics for the dynamic multi-vehicle dial-a-ride problem. Parallel Comput. 30(3), 377–387 (2004). doi:10.1016/j.parco.2003.12.001.CrossRefGoogle Scholar
  2. 2.
    J. Barcelo, H. Grzybowska, S. Pardo, Vehicle routing and scheduling models, simulation and city logistics, in Dynamic Fleet Management, ed. by V. Zeimpekis, C.D. Tarantilis, G.M. Giaglis, I. Minis. Operations Research/Computer Science Interfaces, vol. 38 (Springer, New York, 2007), pp. 163–195Google Scholar
  3. 3.
    A. Beaudry, G. Laporte, T. Melo, S. Nickel, Dynamic transportation of patients in hospitals. OR Spectr. 32, 77–107 (2010). doi:10.1007/s00291-008-0135-6CrossRefGoogle Scholar
  4. 4.
    R. Bent, P. Van Hentenryck, Scenario-based planning for partially dynamic vehicle routing with stochastic customers. Oper. Res. 52(6), 977–987 (2004)CrossRefGoogle Scholar
  5. 5.
    I. Benyahia, J.Y. Potvin, Decision support for vehicle dispatching using genetic programming. IEEE Trans. Syst. Man Cybern. A: Syst. Humans 28(3), 306–314 (1998)CrossRefGoogle Scholar
  6. 6.
    F. Blakeley, B. Arguello, B. Cao, W. Hall, J. Knolmajer, Optimizing periodic maintenance operations for Schindler elevator corporation. Interfaces 33(1), 67–79 (2003). doi:10.1287/inte.33.1.67.12722CrossRefGoogle Scholar
  7. 7.
    Y. Borenstein, N. Shah, E. Tsang, R. Dorne, A. Alsheddy, C. Voudouris, On the partitioning of dynamic workforce scheduling problems. J. Sched. 13(4), 411–425 (2010). doi:10.1007/s10951-009-0152-6CrossRefGoogle Scholar
  8. 8.
    N. Bostel, P. Dejax, P. Guez, F. Tricoire, Multiperiod planning and routing on a rolling horizon for field force optimization logistics, in The Vehicle Routing Problem: Latest Advances and New Challenges, ed. by R. Sharda, B. Golden, S. Raghavan, E. Wasil. Operations Research/Computer Science Interfaces, vol. 43 (Springer, New York, 2008), pp. 503–525Google Scholar
  9. 9.
    M.S. Chang, S. Chen, C. Hsueh, Real-time vehicle routing problem with time windows and simultaneous delivery/pickup demands. J. East. Asia Soc. Transp. Stud. 5, 2273–2286 (2003)Google Scholar
  10. 10.
    B.K.S. Cheung, K.L. Choy, C.-L. Li, W. Shi, J. Tang, Dynamic routing model and solution methods for fleet management with mobile technologies. Int. J. Prod. Econ. 113(2), 694–705 (2008). doi:10.1016/j.ijpe.2007.10.018CrossRefGoogle Scholar
  11. 11.
    J.-F. Cordeau, G. Laporte, F. Pasin, S. Ropke, Scheduling technicians and tasks in a telecommunications company. J. Sched. 13(4), 393–409 (2010). doi:10.1007/s10951-010-0188-7CrossRefGoogle Scholar
  12. 12.
    M. Gendreau, F. Guertin, J.-Y. Potvin, E. Taillard, Parallel tabu search for real-time vehicle routing and dispatching. Transp. Sci. 33(4), 381–390 (1999). doi:10.1287/trsc.33.4.381CrossRefGoogle Scholar
  13. 13.
    A. Haghani, S. Jung, A dynamic vehicle routing problem with time-dependent travel times. Comput. Oper. Res. 32(11), 2959–2986 (2005). doi:10.1016/j.cor.2004.04.013CrossRefGoogle Scholar
  14. 14.
    H. Hashimoto, S. Boussier, M. Vasquez, C. Wilbaut, A GRASP-based approach for technicians and interventions scheduling for telecommunications. Ann. Oper. Res. 183, 143–161 (2011). doi:10.1007/s10479-009-0545–0Google Scholar
  15. 15.
    L. Hong, An improved LNS algorithm for real-time vehicle routing problem with time windows. Comput. Oper. Res. 39(2), 151–163 (2012). doi:10.1016/j.cor.2011.03.006CrossRefGoogle Scholar
  16. 16.
    S. Ichoua, M. Gendreau, J.-Y. Potvin, Diversion issues in real-time vehicle dispatching. Transp. Sci. 34(4), 426–438 (2000). doi:10.1287/trsc.34.4.426.12325CrossRefGoogle Scholar
  17. 17.
    S. Ichoua, M. Gendreau, J.-Y. Potvin, Vehicle dispatching with time-dependent travel times. Eur. J. Oper. Res. 144(2), 379–396 (2003). doi:10.1016/S0377-2217(02)00147–9Google Scholar
  18. 18.
    A. Lackner, Dynamische Tourenplanung mit ausgewählten Metaheuristiken. PhD thesis, Georg-August-Universität Göttingen, 2004Google Scholar
  19. 19.
    K. Lund, O.B.G. Madsen, J.M. Rygaard, Vehicle routing problems with varying degrees of dynamism. Technical report, IMM Institute of Mathematical Modelling (1996)Google Scholar
  20. 20.
    R. Montemanni, L.M. Gambardella, A.E. Rizzoli, A.V. Donati, Ant colony system for a dynamic vehicle routing problem. J. Comb. Optim. 10(4), 327–343 (2005). doi:10.1007/s10878-005-4922-6CrossRefGoogle Scholar
  21. 21.
    V. Pillac, M. Gendreau, C. Guéret, A.L. Medaglia, A review of dynamic vehicle routing problems. Eur. J. Oper. Res. 225(1), 1–11 (2013). doi:10.1016/j.ejor.2012.08.015CrossRefGoogle Scholar
  22. 22.
    V. Pillac, C. Guéret, A.L. Medaglia, A parallel matheuristic for the technician routing and scheduling problem. Optim. Lett. 7(7), 1525–1535 (2013). doi:10.1007/s11590-012-0567-4CrossRefGoogle Scholar
  23. 23.
    D. Pisinger, S. Ropke, A general heuristic for vehicle routing problems. Comput. Oper. Res. 34(8), 2403–2435 (2007). doi:10.1016/j.cor.2005.09.012CrossRefGoogle Scholar
  24. 24.
    D. Pisinger, S. Ropke, Large neighborhood search, in Handbook of Metaheuristics, ed. by M. Gendreau, J.-Y. Potvin. International Series in Operations Research and Management Science, vol. 146 (Springer, New York, 2010), pp. 399–419Google Scholar
  25. 25.
    J.-Y. Potvin, J.-M. Rousseau, A parallel route building algorithm for the vehicle routing and scheduling problem with time windows. Eur. J. Oper. Res. 66(3), 331–340 (1993). doi:10.1016/0377-2217(93)90221-8CrossRefGoogle Scholar
  26. 26.
    C. Prins, Two memetic algorithms for heterogeneous fleet vehicle routing problems. Eng. Appl. Artif. Intell. 22(6), 916–928 (2009). doi:10.1016/j.engappai.2008.10.006CrossRefGoogle Scholar
  27. 27.
    S. Ropke, D. Pisinger, An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows. Transp. Sci. 40(4), 455–472 (2006)CrossRefGoogle Scholar
  28. 28.
    P. Shaw, Using constraint programming and local search methods to solve vehicle routing problems, in Principles and Practice of Constraint Programming – CP98. Lecture Notes in Computer Science, vol. 1520 (Springer, Berlin/Heidelberg, 1998), pp. 417–431Google Scholar
  29. 29.
    M.M. Solomon, Algorithms for the vehicle-routing and scheduling problems with time window constraints. Oper. Res. 35(2), 254–265 (1987)CrossRefGoogle Scholar
  30. 30.
    E.D. Taillard, L.M. Gambardella, M. Gendreau, J.-Y. Potvin, Adaptive memory programming: a unified view of metaheuristics. Eur. J. Oper. Res. 135(1), 1–16 (2001). doi:10.1016/S0377-2217(00)00268-XCrossRefGoogle Scholar
  31. 31.
    H. Tang, E. Miller-Hooks, R. Tomastik, Scheduling technicians for planned maintenance of geographically distributed equipment. Transp. Res. E: Logist. Transp. Rev. 43(5), 591–609 (2007). doi:10.1016/j.tre.2006.03.004CrossRefGoogle Scholar
  32. 32.
    F. Tricoire, Optimisation des Tournées de Véhicules et de Personnels de Maintenance: Application à la Distribution et au Traitement des Eaux. PhD thesis, École Nationale Supérieure des Techniques Industrielles et des Mines de Nantes, 2006Google Scholar
  33. 33.
    E. Tsang, C. Voudouris, Fast local search and guided local search and their application to British Telecom’s workforce scheduling problem. Oper. Res. Lett. 20(3), 119–127 (1997). doi:10.1016/S0167-6377(96)00042-9CrossRefGoogle Scholar
  34. 34.
    J.I. Van Hemert, J.L. Poutré, Dynamic routing problems with fruitful regions: models and evolutionary computation, in Parallel Problem Solving from Nature, ed. by X. Yao, E. Burke, J.A. Lozano, J. Smith, J.J. Merelo-Guervós, J.A. Bullinaria, J. Rowe, P. Tino, A. Kabán, H.-P. Schwefel. Lecture Notes in Computer Science, vol. 3242 (Springer, Berlin/Heidelberg, 2004), pp. 692–701Google Scholar
  35. 35.
    T. Vidal, T.G. Crainic, M. Gendreau, C. Prins, A hybrid genetic algorithm with adaptive diversity management for a large class of vehicle routing problems with time-windows. Comput. Oper. Res. 40(1), 475–489 (2013). doi:10.1016/j.cor.2012.07.018CrossRefGoogle Scholar
  36. 36.
    D. Weigel, B. Cao, Applying GIS and OR techniques to solve Sears technician-dispatching and home delivery problems. Interfaces 29(1), 112–130 (1999). doi:10.1287/inte.29.1.112CrossRefGoogle Scholar
  37. 37.
    J. Xu, S. Chiu, Effective heuristic procedures for a field technician scheduling problem. J. Heuristics 7(5), 495–509 (2001)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Ecole des Mines de NantesNantesFrance
  2. 2.Centro para la Optimizacion y Probabilidad Aplicada (COPA)Universidad de los AndesBogotaColombia
  3. 3.LARISUniversité d’AngersAngersFrance

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