This paper presents strong scenario dominance cuts for effectively solving the multi-stage
stochastic mixed-integer programs (M-SMIPs), specifically focusing on the two most well …

Additive Manufacturing (AM) enhances the flexibility of manufacturing networks. In this
paper, we present a Location-Production-Routing (LPR) problem designed for a distributed …

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

In this study, we present a deep learning-optimization framework to tackle dynamic mixed-
integer programs. Specifically, we develop a bidirectional Long Short Term Memory (LSTM) …

A burgeoning literature shows that self-learning algorithms may, under some conditions,
reach seemingly-collusive outcomes: after repeated interaction, competing algorithms earn …

In this paper, we address the difficulty of solving large-scale multi-dimensional knapsack
instances (MKP), presenting a novel deep reinforcement learning (DRL) framework. In this …

Online reviews are one of the most trusted resources for inferring customer needs and
understanding consumer decision-making behavior. This study attempts to integrate textual …

Abstract Machine learning (ML) algorithms pose significant challenges in predicting
unknown parameters for optimization models in decision-making scenarios. Conventionally …

We investigate a novel type of online sequential decision problem under uncertainty, namely
mixed observability Markov decision process with time-varying interval-valued parameters …

In this study, we develop an innovative data-driven optimization approach to solve the drone
delivery service planning problem with online demand. Drone-based logistics are expected …