A Generalizable MARL-LP Approach for Scheduling in Logistics

A hybrid approach combining Multi-Agent Reinforcement Learning (MARL) and Linear Programming (LP) is proposed to solve complex scheduling and vehicle routing problems in logistics. The primary objective is to develop a generalizable, zero-shot policy that can adapt to new, unseen network conditions without needing to be retrained. This system aims to optimize vehicle utilization and reduce costs by addressing inefficiencies like underloaded trucks and shipment bottlenecks. The discussion also evaluates the shortcomings of alternative methods, such as standard solvers, pure Linear Programming, Genetic Algorithms, and pure Reinforcement Learning, for this dynamic, multi-objective problem.