Multi network cooperative carbon trading supply chain optimization based on situational transfer learning

被引：0
|
作者：
Quo Y. [1 ,2 ]
Chen T. [2 ]
Liu W. [2 ]
机构：
[1] School of Science, School of Big-data Science, Zhejiang University of Science and Technology, Hangzhou
[2] School of Software, Liaoning Technical University, Huludao
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2023年 / 29卷 / 03期
基金：
中国国家自然科学基金;
关键词：
carbon trading; multi network cooperation; network optimization; situational transfer learning; supply chain;
D O I：
10.13196/j.cims.2023.03.028
中图分类号：
学科分类号：
摘要：
To realize the joint optimization of cross-enterprise supply chains with carbon trading, a multi-network bidirectional cooperative integer programming model and a situational transfer learning algorithm were proposed. Aiming at the cooperative decision-making between independent supply chains of different enterprises, the proposed model employed cooperative cost, partner sharing, collaborative transportation and horizontal logistics to establish objective functions on economic and environmental bottom-lines, which were quantified and combined the objectives through quota-based carbon trading. The proposed algorithm integrated the Markov decision process with an information accumulation mechanism, which could effectively accumulate solution experience in different situations and improve the solving efficiency and accuracy. The experimental results based on real data showed that the solving speed of the proposed method was 92.68 times faster than that of the CPLEX solver. The transfer learning method was proven to be superior to the independent learning in both efficiency and accuracy under different scale instances. Moreover, the proposed algorithm showed good compatibility and robustness, and could process large-scale instances effectively. © 2023 CIMS. All rights reserved.
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页码：1001 / 1028
页数：27
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