关键词: 供应链, 多网合作, 碳交易, 情景迁移学习, 网络优化

Abstract: To realize the joint optimization of cross-enterprise supply chains with carbon trading,a multi-network bi-directional 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.

Key words: supply chain, multi network cooperation, carbon trading, situational transfer learning, network optimization