Robust-optimization-guiding deep reinforcement learning for chemical material production scheduling
Journal
Computers and Chemical Engineering
Journal Volume
187
Start Page
108745
ISSN
0098-1354
Date Issued
2024-08
Author(s)
DOI
10.1016/j.compchemeng.2024.108745
Abstract
This study presents a novel guiding framework embedded with robust optimization (RO) for the training phase of reinforcement learning (RL), specifically tailored for dynamic scheduling of a single-stage multi-product chemical reactor in an uncertain environment. The proposed framework addresses the challenge of local optima in policy gradient methods by integrating optimization methods, enhancing both the objective value and learning stability of the RL. We further enhance the robustness of the proposed model against parameter distortions by incorporating RO as a guiding engine. A numerical study of chemical material production scheduling is conducted to validate the proposed model and the results demonstrate the effectiveness to address demand volatility with several metrics including sensitivity analysis, solution quality analysis, computational time, compared to a typical actor-critic RL.
Subjects
Chemical production
Production scheduling
Reinforcement learning
Robust optimization
Stochastic programming
Publisher
Elsevier BV
Type
journal article