带时间窗和容量约束的取送货路径问题的深度强化学习解决方案
Deep Reinforcement Learning solution for pickup and delivery routing problems with time window and capacity constraints
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中文总结 AI 辅助
本研究首次将改进的JAMPR深度强化学习模型用于求解带容量和时间窗约束的取送货路径问题，可为中小规模问题提供快速最优解，为超大规模问题提供快速次优解。
中文摘要 AI 辅助
在全球城市人口增长背景下，为货物取送货构建车辆最优路径是极具前景的任务之一。尽管这类小规模问题可通过多种经典方法解决，但针对中大规模问题，在容量、时间窗等现实约束下实现快速（或实时）路径优化仍是极具挑战性的任务。本研究首次成功将深度强化学习方法（改进的JAMPR模型）应用于求解带容量和时间窗约束的取送货问题（CPDPTW），得到的鲁棒模型可为中小规模问题提供快速最优解，为规模大于200的更大规模问题提供快速次优解。
英文摘要
The task of constructing vehicles optimal routes for pickup and delivery of goods is one of most promising tasks in the context of global urban population growth. Although this kind of problems with small size can be solved by various classical approaches, a fast (or realtime) route optimizer under the constraints of the real world (such as capacity and time windows constraints) for medium-large size problems still remains a highly challenging task. In this work we, for the first time, successfully applied a deep Reinforcing Learning approach (modified JAMPR model) to solve Pickup and Delivery problem with Capacity and Time Window constraints (CPDPTW). We obtained a robust model that gives a fast optimal solution for problems of small and medium size, and gives fast suboptimal solution for problems of larger (> 200) size.
发表机构
- Moscow State University（莫斯科国立大学）
- Space Research Institute of RAS（俄罗斯科学院空间研究所）
机构由 AI 辅助整理，请以论文原文为准。
