Determinism versus uncertainty : Examining the worst-case expected performance of data-driven policies

Journal article


Tian, Xuecheng, Wang, Shuaian, Laporte, Gilbert and Yang, Ying. (2024). Determinism versus uncertainty : Examining the worst-case expected performance of data-driven policies. European Journal of Operational Research. 318(1), pp. 242-252. https://doi.org/10.1016/j.ejor.2024.04.031
AuthorsTian, Xuecheng, Wang, Shuaian, Laporte, Gilbert and Yang, Ying
Abstract

This paper explores binary decision making, a critical domain in areas such as finance and supply chain management, where decision makers must often choose between a deterministic-cost option and an uncertain-cost option. Given the limited historical data on the uncertain cost and its unknown probability distribution, this research aims to ascertain how decision makers can optimize their decisions. To this end, we evaluate the worst-case expected performance of all possible data-driven policies, including the sample average approximation policy, across four scenarios differentiated by the extent of knowledge regarding the lower and upper bounds of the first moment of the uncertain cost distribution. Our analysis, using worst-case expected absolute regret and worst-case expected relative regret metrics, consistently shows that no data-driven policy outperforms the straightforward strategy of choosing either a deterministic-cost or uncertain-cost option in these scenarios. Notably, the optimal choice between these two options depends on the specific lower and upper bounds of the first moment. Our research contributes to the literature by revealing the minimal worst-case expected performance of all possible data-driven policies for binary decision-making problems.

KeywordsDecision analysis ; Data-driven optimization ; Sample average approximation ; Worst-case expected performance
Year01 Jan 2024
JournalEuropean Journal of Operational Research
Journal citation318 (1), pp. 242-252
PublisherElsevier Science BV
ISSN0377-2217
Digital Object Identifier (DOI)https://doi.org/10.1016/j.ejor.2024.04.031
Open accessPublished as non-open access
Research or scholarlyResearch
Page range242-252
Publisher's version
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All rights reserved
File Access Level
Controlled
Output statusPublished
Publication dates
Online27 Apr 2024
Publication process dates
Accepted26 Apr 2024
Deposited04 Jul 2024
Additional information

© 2024 Elsevier B.V. All rights reserved.

Place of publicationNetherlands
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