This transition moves inventory planning away from static safety stock rules toward more flexible policy structures that ...

Abstract: This paper presents a multistage stochastic programming formulation of a transmission-constrained economic dispatch subject to multiarea renewable production uncertainty, with a focus on ...

Abstract: This paper presents a computation-efficient stochastic dynamic programming algorithm for solving energy storage price arbitrage considering variable charge and discharge efficiencies. We ...

Stochastic processes involving randomness dominate in many areas of natural and man made systems. Describing their evolution quantitatively requires powerful theory from the fields of probability, ...

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 ...

Without resorting to dynamic programming, we determine the decumulation strategy for the holder of a defined contribution pension plan. We formulate this as a constrained stochastic optimal control ...

ABSTRACT: Offline reinforcement learning (RL) focuses on learning policies using static datasets without further exploration. With the introduction of distributional reinforcement learning into ...

This project implements a Dynamic Programming (DP) solution for optimal inventory control, inspired by fundamental principles in Dimitri Bertsekas's work on "Lessons from AlphaZero for Optimal, Model ...