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Partially adaptive multistage stochastic programming
Journal article   Peer reviewed

Partially adaptive multistage stochastic programming

Sezen Ece Kayacık, Beste Basciftci, Albert H. Schrotenboer and Evrim Ursavas
European journal of operational research, Vol.321(1), pp.192-207
02/2025
DOI: 10.1016/j.ejor.2024.09.034

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Abstract

Multistage stochastic programming is a powerful tool allowing decision-makers to revise their decisions at each stage based on the realized uncertainty. However, organizations are not able to be fully flexible, as decisions cannot be revised too frequently in practice. Consequently, decision commitment becomes crucial to ensure that initially made decisions remain unchanged for a certain period of time. This paper introduces partially adaptive multistage stochastic programming, a new optimization paradigm that strikes an optimal balance between decision flexibility and commitment by determining the best stages to revise decisions depending on the allowed level of flexibility. We introduce a novel mathematical formulation and theoretical properties eliminating certain constraint sets. Furthermore, we develop a decomposition method that effectively handles mixed-integer partially adaptive multistage programs by adapting the integer L-shaped method and Benders decomposition. Computational experiments on stochastic lot-sizing and generation expansion planning problems show substantial advantages attained through optimal selections of revision times when flexibility is limited, while demonstrating computational efficiency attained by employing the proposed properties and solution methodology. By adhering to these optimal revision times, organizations can achieve performance levels comparable to fully flexible settings. •A partially adaptive multistage stochastic program is introduced to optimize revision times.•Theoretical properties are proposed to eliminate non-anticipativity constraints.•A decomposition method is developed to solve mixed-integer partially adaptive multistage programs.•Computational experiments on lot-sizing and generation expansion planning problems are conducted.•The results show comparable performance to fully flexible settings through optimized revision times.
 Generation expansion planning Lot-sizing Mixed-integer programming Multistage stochastic optimization Stochastic programming

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