Abstract: There is a wide range of problems in energy systems that require making decisions in the presence of different forms of uncertainty. The fields that address sequential, stochastic decision ...

Many Optimization problems in engineering and economic involve the challenging task of pondering both conflicting goals and random data. In this paper, we give an up-to-date overview of how important ...

Abstract: Dynamic Programming (DP) provides a powerful framework for modeling complex decision problems where uncertainty is resolved and decisions are made over time. But it is difficult to scale to ...

ABSTRACT: The study of unit commitment (UC) aims to find reasonable schedules for generators to optimize power systems’ operation. Many papers have been published that solve UC through different ...

Multistage stochastic mixed-integer linear problems (MSMILPs) are non-convex, usually of large scale, and hard to solve. This calls for decomposition approaches to keep the solution process ...

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This study develops a unified framework for optimal portfolio selection in jump–uncertain stochastic markets, contributing both theoretical foundations and computational insights. We establish the ...