Advertisement

A Set Covering Approach for the Pickup and Delivery Problem with General Constraints on Each Route

  • Hideki Hashimoto
  • Youichi Ezaki
  • Mutsunori Yagiura
  • Koji Nonobe
  • Toshihide Ibaraki
  • Arne Løkketangen
Conference paper
  • 588 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4638)

Abstract

We consider a generalization of the pickup and delivery problem with time windows by allowing general constraints on each route, and propose a heuristic algorithm based on the set covering approach, in which all requests are required to be covered by a set of feasible routes. Our algorithm first generates a set of feasible routes, and repeats reconstructing of the set by using information from a Lagrangian relaxation of the set covering problem corresponding to the set. The algorithm then solves the resulting set covering problem instance to find a good feasible solution for the original problem. We conduct computational experiments for instances with various constraints and confirm the flexibility and robustness of our algorithm.

Keywords

Lagrangian Relaxation Nonrenewable Resource Metaheuristic Algorithm Delivery Problem Good Feasible Solution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bent, R., Hentenryck, P.V.: A two-stage hybrid algorithm for pickup and delivery vehicle routing problems with time windows. Computers and Operations Research 33, 875–893 (2006)zbMATHCrossRefGoogle Scholar
  2. 2.
    Ropke, S., Pisinger, D.: An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows. Transportation science 40(4), 455–472 (2006)CrossRefGoogle Scholar
  3. 3.
    Li, H., Lim, A.: A metaheuristic for the pickup and delivery problem with time windows. International Journal on Artificial Intelligence Tools 12(2), 173–186 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Hideki Hashimoto
    • 1
  • Youichi Ezaki
    • 2
  • Mutsunori Yagiura
    • 3
  • Koji Nonobe
    • 4
  • Toshihide Ibaraki
    • 5
  • Arne Løkketangen
    • 6
  1. 1.Graduate School of Informatics, Kyoto University, KyotoJapan
  2. 2.Canon System Solutions Inc.Japan
  3. 3.Graduate School of Information Science, Nagoya University, NagoyaJapan
  4. 4.Faculty of Engineering, Hosei University, KoganeiJapan
  5. 5.School of Science and Technology, Kwansei Gakuin University, SandaJapan
  6. 6.Molde University College, MoldeNorway

Personalised recommendations