Advertisement

TSP with Multiple Time-Windows and Selective Cities

  • Marta Mesquita
  • Alberto Murta
  • Ana Paias
  • Laura Wise
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8197)

Abstract

We address a special TSP in which the set of cities is partitioned into two subsets: mandatory cities and selective cities. All mandatory cities should be visited once within one of the corresponding predefined multiple time windows. A subset of the selective cities, whose cardinality depends on the tour completion time, should be visited within one of the associated multiple time windows. The objective is to plan a tour, not exceeding a predefined number of days, that minimizes a linear combination of the total traveled distance as well as the total waiting time. We present a mixed integer linear programming (MILP) model for the problem and propose a heuristic approach to solve it. Computational experiments address two real world problems that arise in different practical contexts.

Keywords

Traveling salesman problem multiple time windows mixed integer linear programming branch-and-bound heuristics 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Fischetti, M., Salazar-González, J.-J., Toth, P.: The generalized traveling salesman and orienteering problems. In: Gutin, G., Punnen, A.P. (eds.) The Traveling Salesman Problem and its Variants, pp. 609–662. Kluwer (2002)Google Scholar
  2. 2.
    Gendreau, M., Laporte, G., Semet, F.: A branch-and-cut algorithm for the undirected selective traveling salesman problem. Networks 32(4), 263–273 (1998)MathSciNetCrossRefMATHGoogle Scholar
  3. 3.
    Kaufman, L., Rousseeuw, P.J.: Finding groups in data: an introduction to cluster analysis. Wiley, New York (1990)CrossRefGoogle Scholar
  4. 4.
    Labadie, N., Mansini, R., Melechovsky, J., Calvo, R.W.: The team orienteering problem with time windows: An LP-based granular variable neighborhood search. European Journal of Operational Research 220, 15–27 (2012)MathSciNetCrossRefMATHGoogle Scholar
  5. 5.
    Pisinger, D., Ropke, S.: A general heuristic for vehicle routing problems. Computers & Operations Research 34, 2403–2435 (2007)MathSciNetCrossRefMATHGoogle Scholar
  6. 6.
    Ropke, S., Pisinger, D.: An adaptative large neighborhood search heuristic for the pickup and delivery problem with time windows. Transportation Science 40(4), 455–472 (2006)CrossRefGoogle Scholar
  7. 7.
    Tricoire, F., Romauch, M., Doerner, K.F., Hartl, R.F.: Heuristics for the multi-period orienteering problem with multiple time windows. Computers & Operations Research 37, 351–367 (2010)MathSciNetCrossRefMATHGoogle Scholar
  8. 8.
    Vansteenwegen, P., Souffriau, W., Berghe, G.V., Oudheusden, D.V.: Iterated local search for the team orienteering problem with time windows. Computers & Operations Research 36, 3281–3290 (2009)CrossRefMATHGoogle Scholar
  9. 9.
    Vansteenwegen, P., Souffriau, W., Oudheusden, D.V.: The orienteering problem: A survey. European Journal of Operational Research 209, 1–10 (2011)MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Marta Mesquita
    • 1
  • Alberto Murta
    • 2
  • Ana Paias
    • 3
  • Laura Wise
    • 2
  1. 1.CIO and ISAUTLLisboaPortugal
  2. 2.IPMA, Portuguese Institute of the Sea and AtmosphereLisboaPortugal
  3. 3.CIO and DEIO, Faculdade de CiênciasUniversidade de LisboaLisboaPortugal

Personalised recommendations