Collaborative production control and distributor selection via multi-agent reinforcement learning with differentiable communication
Year: 2025
Type: article
Abstract: Collaborative production control and distributor selection are essential for resource allocation and meeting the core of Industry 5.0’s human-centric vision. However, traditional approaches typically handle these decisions independently, failing to adequately address fluctuating market conditions, demand uncertainty, and varying distributor competencies. This paper integrates production control and distributor selection as a Partially Observable Markov Decision Process (POMDP) in a multi-agent system. Specifically, a production control agent optimizes outputs by balancing inventory levels and opportunity costs, while a distributor selection agent dynamically adjusts allocations considering workforce skill diversity, cost efficiency , and equity. The formulated POMDP is solved using a multi-agent reinforcement learning (MARL) framework featuring a differentiable communication layer and GRU-based recurrent neural networks . Numerical experiments conducted under both stable and highly volatile market conditions demonstrate the proposed system’s enhanced adaptability and responsiveness. In particular, inter-agent messaging communication leading to improved welfare metrics and robust performance under diverse distributor-weight configurations. Notably, the resulting system promotes equitable distributor involvement, aligning with Industry 5.0’s emphasis on sustainable, people-centric supply chain operations.
Source: Expert Systems with Applications
Authors: Yang Deng, Guojun Sheng, Andy H.F. Chow, Zhili Zhou, Qinyang Bai, Zicheng Su
Language: English
Subfield: Management Information Systems
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $3,500
OpenAlex ID: W4409452182