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Adapting Branching and Queuing for Multi-objective Branch and Bound

In: Operations Research Proceedings 2023

Author

Listed:
  • Julius Bauß

    (University of Wuppertal)

  • Michael Stiglmayr

    (University of Wuppertal)

Abstract

Branch and bound algorithms have to cope with several additional difficulties in the multi-objective case. Not only the bounding procedure is considerably weaker, but also the handling of upper and lower bound sets requires much more computational effort since both sets can be of exponential size. Thus, the order in which the subproblems are considered is of particular importance. Thereby, it is crucial not only to find efficient solutions as soon as possible but also to find a set of (efficient) solutions whose images are well distributed along the non-dominated frontier. In this paper we evaluate the performance of multi-objective branch and bound algorithms depending on branching and queuing of subproblems. We use, e.g., the hypervolume indicator as a measure for the gap between lower and upper bound set to implement a multi-objective best-first strategy. We test our approaches on multi-objective knapsack and generalized assignment problems.

Suggested Citation

  • Julius Bauß & Michael Stiglmayr, 2025. "Adapting Branching and Queuing for Multi-objective Branch and Bound," Lecture Notes in Operations Research, in: Guido Voigt & Malte Fliedner & Knut Haase & Wolfgang Brüggemann & Kai Hoberg & Joern Meissner (ed.), Operations Research Proceedings 2023, chapter 0, pages 535-541, Springer.
    • Handle: RePEc:spr:lnopch:978-3-031-58405-3_68
    DOI: 10.1007/978-3-031-58405-3_68
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