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Bi-objective Orienteering: Towards a Dynamic Multi-objective Evolutionary Algorithm

  • Jakob Bossek
  • Christian GrimmeEmail author
  • Stephan Meisel
  • Günter Rudolph
  • Heike Trautmann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11411)

Abstract

We tackle a bi-objective dynamic orienteering problem where customer requests arise as time passes by. The goal is to minimize the tour length traveled by a single delivery vehicle while simultaneously keeping the number of dismissed dynamic customers to a minimum. We propose a dynamic Evolutionary Multi-Objective Algorithm which is grounded on insights gained from a previous series of work on an a-posteriori version of the problem, where all request times are known in advance. In our experiments, we simulate different decision maker strategies and evaluate the development of the Pareto-front approximations on exemplary problem instances. It turns out, that despite severely reduced computational budget and no oracle-knowledge of request times the dynamic EMOA is capable of producing approximations which partially dominate the results of the a-posteriori EMOA and dynamic integer linear programming strategies.

Keywords

Multi-objective optimization Metaheuristics Vehicle routing Combinatorial optimization Dynamic optimization 

Notes

Acknowledgments

J. Bossek, C. Grimme, S. Meisel and H. Trautmann acknowledge support by the European Research Center for Information Systems (ERCIS).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jakob Bossek
    • 1
  • Christian Grimme
    • 1
    Email author
  • Stephan Meisel
    • 1
  • Günter Rudolph
    • 2
  • Heike Trautmann
    • 1
  1. 1.Department of Information SystemsUniversity of MünsterMünsterGermany
  2. 2.Department of Computer ScienceTU Dortmund UniversityDortmundGermany

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