Advancing Sustainability in Global Supply Chains through Agent-based Simulation

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Publication Date: 5/30/2024

Event: The Eighteenth International Conference on Digital Society (ICDS 2024)

Reference: pp. 33-39, 2024

Authors: Haoyu Wang, NEC Laboratories America, Inc.; Erhu He, University of Pittsburgh; Can Zheng, University of Pittsburgh; Lu-An Tang, NEC Laboratories America, Inc.; Zhengzhang Chen, NEC Laboratories America, Inc.; Xujiang Zhao, NEC Laboratories America, Inc.; Xiaowei Jia, University of Pittsburgh; Haifeng Chen, NEC Laboratories America, Inc.

Abstract: In today’’s world, with its complex global supply chains, the difficulties and uncertainties we face offer both challenges and opportunities for making things better, especially in terms of efficiency and sustainability. These challenges grow due to unpredictable events, such as natural disasters, unexpected incidents, and unusual business practices, pushing us towards more advanced modeling methods that focus on reducing risks and enhancing sustainability. In this paper, we present a new agent-based simulation approach that goes beyond the usual limits of supply chain simulations by incorporating sustainability directly into supply chain operations using reinforcement learning (RL) algorithms. We introduce MOGI, a sustainable supply chain simulation system that takes carbon emissions into account in its main operations. Additionally, we examine how effective a multi-agent RL strategy is in dealing with the complex and uncertain nature of supply chains that span multiple levels. By comparing this strategy with traditional heuristic methods, our study looks at how well single versus multiple RL agents can manage risks and improve sustainability in both the beginning and end parts of the supply chain. The results of our experiments show that strategies based on RL are much better than traditional methods at managing risks, making profits, and achieving sustainability goals.

Publication Link: https://www.thinkmind.org/library/ICDS/ICDS_2024/icds_2024_2_20_10013.html