Multi-objective minmax robust combinatorial optimization with cardinality-constrained uncertainty
Raith A, Schmidt M, Schoebel A, Thom L (2018)
Publication Type: Journal article
Publication year: 2018
Journal
Book Volume: 267
Pages Range: 628-642
Journal Issue: 2
DOI: 10.1016/j.ejor.2017.12.018
Abstract
In this paper, we develop two approaches to find minmax robust efficient solutions for multi-objective combinatorial optimization problems with cardinality-constrained uncertainty. First, we extend an existing algorithm for the single-objective problem to multi-objective optimization. We propose also an enhancement to accelerate the algorithm, even for the single-objective case, and we develop a faster version for special multi-objective instances. Second, we introduce a deterministic multi-objective problem with sum and bottleneck functions, which provides a superset of the robust efficient solutions. Based on this, we develop a label setting algorithm to solve the multi-objective uncertain shortest path problem. We compare both approaches on instances of the multi-objective uncertain shortest path problem originating from hazardous material transportation.
Involved external institutions
University of Auckland
New Zealand (NZ)
Erasmus University Rotterdam (EUR) / Erasmus Universiteit Rotterdam
Netherlands (NL)
Georg-August-Universität Göttingen
Germany (DE)
New Zealand (NZ)
Erasmus University Rotterdam (EUR) / Erasmus Universiteit Rotterdam
Netherlands (NL)
Georg-August-Universität Göttingen
Germany (DE)
How to cite
APA:
Raith, A., Schmidt, M., Schoebel, A., & Thom, L. (2018). Multi-objective minmax robust combinatorial optimization with cardinality-constrained uncertainty. European Journal of Operational Research, 267(2), 628-642. https://doi.org/10.1016/j.ejor.2017.12.018
MLA:
Raith, Andrea, et al. "Multi-objective minmax robust combinatorial optimization with cardinality-constrained uncertainty." European Journal of Operational Research 267.2 (2018): 628-642.
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