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

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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.
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Issue Date
2019-02
Language
English
Article Type
Article
Citation

COMPUTERS & CHEMICAL ENGINEERING, v.121, pp.556 - 573

ISSN
0098-1354
DOI
10.1016/j.compchemeng.2018.11.020
URI
http://hdl.handle.net/10203/254142
Appears in Collection
CBE-Journal Papers(저널논문)
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