Multi-objective minmax robust combinatorial optimization with cardinality-constrained uncertainty

Andrea Raith, Marie Schmidt, Anita Schöbel and Lisa Thom

European Journal of Operational Research, 2018, vol. 267, issue 2, 628-642

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.

Keywords: Multiple objective programming; Robust optimization; Combinatorial optimization; Multi-objective robust optimization; Shortest path problem (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:267:y:2018:i:2:p:628-642

DOI: 10.1016/j.ejor.2017.12.018

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