Talking Trucks: Decentralized Collaborative Multi-Agent Order Scheduling for Self-Organizing Logistics
Keywords:Self-organizing Logistics, Multi-agent Systems, Decentralized Decision-making, Collaborative Decision-making, Distributed Constraint Optimization, Order Scheduling, Reinforcement Learning, Digital Twins
AbstractLogistics planning is a complex optimization problem involving multiple decision makers. Automated scheduling systems offer support to human planners; however state-of-the-art approaches often employ a centralized control paradigm. While these approaches have shown great value, their application is hindered in dynamic settings with no central authority. Motivated by real-world scenarios, we present a decentralized approach to collaborative multi-agent scheduling by casting the problem as a Distributed Constraint Optimization Problem (DCOP). Our model-based heuristic approach uses message passing with a novel pruning technique to allow agents to cooperate on mutual agreement, leading to a near-optimal solution while offering low computational costs and flexibility in case of disruptions. Performance is evaluated in three real-world field trials with a logistics carrier and compared against a centralized model-free Deep Q-Network (DQN)-based Reinforcement Learning (RL) approach, a Mixed-Integer Linear Programming (MILP)-based solver, and both human and heuristic baselines. The results demonstrate that it is feasible to have virtual agents make autonomous decisions using our DCOP method, leading to an efficient distributed solution. To facilitate further research in Self-Organizing Logistics (SOL), we provide a novel real-life dataset.
How to Cite
Pingen, G. L. J., van Ommeren, C. R., van Leeuwen, C. J., Fransen, R. W., Elfrink, T., de Vries, Y. C., Karunakaran, J., Demirović, E., & Yorke-Smith, N. (2022). Talking Trucks: Decentralized Collaborative Multi-Agent Order Scheduling for Self-Organizing Logistics. Proceedings of the International Conference on Automated Planning and Scheduling, 32(1), 480-489. https://doi.org/10.1609/icaps.v32i1.19834
Industry and Applications Track