A reinforcement learning-based multi-agent framework applied for solving routing and scheduling problems

作者：

Highlights：

• Multi-agent framework for optimization using metaheuristics.

• Agents modify their actions using concepts of Reinforcement Learning.

• Learning ability of the agents directly influences the quality of solutions.

• Framework validated using Vehicle Routing Problem with Time-Windows (VRPTW) and Unrelated Parallel Machine Scheduling Problem with Sequence-Dependent Setup Times (UPMSP-ST).

摘要

•Multi-agent framework for optimization using metaheuristics.•Agents modify their actions using concepts of Reinforcement Learning.•Learning ability of the agents directly influences the quality of solutions.•Framework validated using Vehicle Routing Problem with Time-Windows (VRPTW) and Unrelated Parallel Machine Scheduling Problem with Sequence-Dependent Setup Times (UPMSP-ST).

论文关键词：Multi-agent framework for optimization,Reinforcement learning,Metaheuristics,Multi-agent systems,Vehicle routing problem with time window,Unrelated parallel machine scheduling problem

论文评审过程：Received 6 February 2018, Revised 24 April 2019, Accepted 24 April 2019, Available online 24 April 2019, Version of Record 30 April 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.04.056