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

A Raith, Marie Schmidt, A Schoebel, Lisa Thom

Research output: Contribution to journalArticleAcademicpeer-review

19 Citations (Scopus)

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
Original languageEnglish
Pages (from-to)628-642
Number of pages15
JournalEuropean Journal of Operational Research
Volume267
Issue number2
DOIs
Publication statusPublished - 18 Dec 2017

Fingerprint

Dive into the research topics of 'Multi-objective minmax robust combinatorial optimization with cardinality-constrained uncertainty'. Together they form a unique fingerprint.

Cite this