Collaborative planning of multi-tier sustainable supply chains: A reinforcement learning enhanced heuristic approach

Despite the growing importance of collaboration in achieving sustainability-related advantages for companies, existing studies lack a systematic framework to determine how multiple supply chains can jointly facilitate strategical decision-making to achieve the objectives in the triple-bottom-line (3BL). In this study, we suggest a comprehensive mixed-integer linear programming model for multi-network collaboration considering 3BL sustainability indicators and develop a heuristic approach enhanced by reinforcement learning to solve the model. The proposed model allows for optimal decision-making across multiple sustainable supply chains, simultaneously minimizing total costs and environmental impacts as well as maximizing social responsibility. The heuristic algorithm integrates a Markov decision-making process and information accumulation mechanism with the exploration of the solution space. It effectively learns from the solving process, and applies the most appropriate operator to iteratively improve the current solution according to the knowledge learnt. Extensive experiments based on real-world data are conducted and the results demonstrate that the proposed model and solution framework yield an effective collaborative supply chain design for each actor with superior efficiency and accuracy. Compared with CPLEX, the average solving times for medium-to-large instance scales are reduced by 16.34% to 87.59%, and 86.67% of the instances saw an improvement of solution quality, with an average improvement of 5.67%. Moreover, the inclusion of horizontal transportation in the proposed model provides a significant improvement of 51.24% in the economic bottom-line, as well as an improvement of 3.42% in the environmental bottom-line.