Multi-timescale, multi-period decision-making model development by combining reinforcement learning and mathematical programming

作     者：Shin, Joohyun Lee, Jay H. 

作者机构：Korea Adv Inst Sci & Technol Chem & Biomol Engn Dept Daejeon South Korea 

出 版 物：《COMPUTERS & CHEMICAL ENGINEERING》 (计算机与化工)

年 卷 期：2019年第121卷

页      面：556-573页

核心收录：

学科分类：0817[工学-化学工程与技术] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Multi-timescale decision making Decision under uncertainty Markov decision process Mathematical programming Reinforcement learning 

摘      要：This study focuses on the linkage between decision layers that have different time scales. The resulting expansion of the boundary of decision-making process can provide more robust and flexible management and operation strategies by resolving inconsistencies between different levels. For this, we develop a multi-timescale decision-making model that combines Markov decision process (MDP) and mathematical programming (MP) in a complementary way and introduce a computationally tractable solution algorithm based on reinforcement learning (RL) to solve the MP-embedded MDP problem. To support the integration of the decision hierarchy, a data-driven uncertainty prediction model is suggested which is valid across all time scales considered. A practical example of refinery procurement and production planning is presented to illustrate the proposed method, along with numerical results of a benchmark case study. (C) 2018 Elsevier Ltd. All rights reserved.