Erin K. Doolittle, Garrett M. Dranichak, Karyn Muir, M. Wiecek
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A note on robustness of the min-max solution to multi-objective linear programs
The challenge of using scalarising methods in multi-objective optimisation results from the choice of the method, which may not be apparent, and given that a method has been selected, from the choice of the values of the scalarising parameters. In general, these values may be unknown and the decision maker faces a difficult situation of making a choice possibly under a great deal of uncertainty. Due to its effectiveness, the robust optimisation approach of Ben-Tal and Nemirovski is applied to resolve the uncertainty carried in scalarised multi-objective linear programs (MOLPs). A robust counterpart is examined for six different scalarisations of the MOLP yielding robust (weakly) efficient solutions to the original MOLP. The study reveals that the min-max optimal solution emerges as a robust (weakly) efficient solution for five out of the six scalarisations. The implications of this result are also discussed.
期刊介绍:
IJMCDM is a scholarly journal that publishes high quality research contributing to the theory and practice of decision making in ill-structured problems involving multiple criteria, goals and objectives. The journal publishes papers concerning all aspects of multicriteria decision making (MCDM), including theoretical studies, empirical investigations, comparisons and real-world applications. Papers exploring the connections with other disciplines in operations research and management science are particularly welcome. Topics covered include: -Artificial intelligence, evolutionary computation, soft computing in MCDM -Conjoint/performance measurement -Decision making under uncertainty -Disaggregation analysis, preference learning/elicitation -Group decision making, multicriteria games -Multi-attribute utility/value theory -Multi-criteria decision support systems and knowledge-based systems -Multi-objective mathematical programming -Outranking relations theory -Preference modelling -Problem structuring with multiple criteria -Risk analysis/modelling, sensitivity/robustness analysis -Social choice models -Theoretical foundations of MCDM, rough set theory -Innovative applied research in relevant fields