Energy emergency supply chain collaboration optimization with group consensus through reinforcement learning considering non-cooperative behaviours

Liu Xiang

Energy, 2020, vol. 210, issue C

Abstract: As a response to emergency events occurred frequently around the world, energy emergency supply chain collaboration has becomes a business imperative with multiple energy trading organizations to respond it by group consensus. However, managing agile energy emergency supply chain collaboration, with the minimum energy recovery time regarding energy supply shortage driven by urgent events such as earthquake, is confronted with a difficult task: govern non-cooperative behaviours of energy emergency supply chain collaboration that identifies the irrational causes underlying deviations from neoclassical utility-maximizing economic decisions. In this paper, develop a smart model for energy emergency supply chain collaboration that the work bridges the divide between emergency supply chain collaboration optimization with group consensus and reinforcement learning. It sets up collaboration consensus with scenarios learning algorithm driven by the satisfaction-level combination of generalising past experience and future scenarios to new local energy supply shortage emergency situations to govern non-cooperative irrational behaviours, resulting in the response process with the minimum energy recovery time, cost and CO2 emissions. Simulations results show that proposed model has a significantly lower running time by 40%, and reduces minimisation of cost for energy restoration by 7% and minimisation of CO2 emissions by 10.8% on average.

Keywords: Energy emergency; Supply chain collaboration; Optimization; Consensus; Non-cooperative behaviours; Reinforcement learning (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:210:y:2020:i:c:s0360544220317059

DOI: 10.1016/j.energy.2020.118597

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