An efficient manufacturing system is key to maintaining a healthy economy today. With the
rapid development of science and technology and the progress of human society, the …

Stochastic Programming is a powerful modeling framework for decision-making under
uncertainty. In this work, we tackle two-stage stochastic programs (2SPs), the most widely …

In this study, we present a deep reinforcement learning framework for solving scenario-
based two-stage stochastic programming problems. Stochastic programs have numerous …

Abstract Instance-specific Algorithm Configuration (AC) methods are effective in
automatically generating high-quality algorithm parameters for heterogeneous NP-hard …

We present a non-anticipative learning-and scenario-based prediction-optimization
(ScenPredOpt) framework that combines deep learning, heuristics, and mathematical …

Two-stage Stochastic Programming (2SP) is a standard framework for modeling decision-
making problems under uncertainty. While numerous methods exist, solving such problems …

Stochastic Programming is a powerful framework that addresses decision-making under
uncertainties, which is a frequent occurrence in real-world problems. To effectively solve …

Two-stage stochastic programs (2SPs) are important tools for making decisions under
uncertainty. Decision-makers use contextual information to generate a set of scenarios to …

We propose a framework for applying reinforcement learning to contextual two-stage
stochastic optimization and apply this framework to the problem of energy market bidding of …

Optimization models have become a cornerstone of the operations research and
prescriptive analytics community. In practice, model parameters are seldom known with …