多目标线性规划最小-最大解的鲁棒性

Q4 Business, Management and Accounting International Journal of Multicriteria Decision Making Pub Date : 2016-01-01 DOI:10.1504/IJMCDM.2016.081390
Erin K. Doolittle, Garrett M. Dranichak, Karyn Muir, M. Wiecek
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引用次数: 5

摘要

在多目标优化中使用缩放方法的挑战来自方法的选择,这可能不明显,并且假设已经选择了一种方法,则来自缩放参数值的选择。一般来说,这些价值可能是未知的,决策者可能面临着在很大的不确定性下做出选择的困难局面。由于其有效性,本-塔尔和涅米洛夫斯基的鲁棒优化方法被用于解决标量多目标线性规划(molp)中所携带的不确定性。对MOLP的六种不同的缩放进行了鲁棒对偶检验,得到了原始MOLP的鲁棒(弱)有效解。研究表明,最小-最大最优解作为鲁棒(弱)有效解出现在六个标量中的五个。本文还讨论了这一结果的意义。
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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.
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来源期刊
International Journal of Multicriteria Decision Making
International Journal of Multicriteria Decision Making Business, Management and Accounting-Strategy and Management
CiteScore
0.70
自引率
0.00%
发文量
9
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
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