版权所有:内蒙古大学图书馆 技术提供:维普资讯• 智图
内蒙古自治区呼和浩特市赛罕区大学西街235号 邮编: 010021
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.