Towards Self-organized Logistics Planning in Decentralised Production Systems via Agent-Based Modelling

The growing number of product variants reduces the utilization of traditional linear assembly systems and increases the cost of customization for manufacturing companies. Matrix-structured Manufacturing Systems (MMS) with freely linked production modules enable efficient use of assembly resources through flexible product routing. However, the high degree of freedom requires greater reliability of the production control system. Traditional centralized production control faces high uncertainty and computational complexity of flexible production. To address these challenges, this study proposes the concept of a decentralized logistics planning system based on agent-based modelling (ABM) and mobile robotic swarms. Decentralization will be achieved by treating robots and workstations as agents that can communicate and interact with each other. The rules of interactions and communications will be tested via ABM and, later, programming the rules into swarming robots to achieve fully autonomous logistics planning. This paper proposes a logistics planning framework with high adaptability and robustness for the matrix assembly system. We offer a hybrid optimisation-simulation algorithmic architecture to industrial practitioners to illustrate the steps and potential benefits of implementing self-organizing logistics planning. This self-organizing system can dynamically adjust logistics planning according to the interactive topology of ABM, which realizes flexible product routing with adaptability to changing production environments.