The Multi-Commodity Pickup and Delivery Vehicle Routing Problem aims to optimize the pickup and delivery of multiple unique commodities using a fleet of several agents with limited payload capacities. This paper addresses the challenge of quickly recomputing the solution to this NP-hard problem when there are unexpected perturbations to the nominal task definitions, likely to occur under real-world operating conditions. The proposed method first decomposes the nominal problem by constructing a search tree using Monte Carlo Tree Search for task assignment, and uses a rapid heuristic for routing each agent. When changes to the problem are revealed, the nominal search tree is rapidly updated with new costs under the updated problem parameters, generating solutions quicker and with a reduced optimality gap, as compared to recomputing the solution as an entirely new problem. Computational experiments are conducted by varying the locations of the nominal problem and the payload capacity of an agent to demonstrate the effectiveness of utilizing the nominal search tree to handle perturbations for real-time implementation.

翻译：多商品取送货车辆路径问题旨在利用有限载荷容量的多智能体车队，优化多种独特商品的取送过程。本文针对在真实运行条件下，标称任务定义出现意外扰动时需快速重新计算该NP难问题解的挑战。所提方法首先通过蒙特卡洛树搜索构建任务分配搜索树以分解标称问题，并采用快速启发式算法规划各智能体路径。当问题参数发生变化时，标称搜索树会根据更新后的参数快速折算新代价，相较于将问题视为全新实例重新求解，该方法能以更小的最优性差距更快生成解。通过改变标称问题中任务点位置及智能体载荷容量进行计算实验，验证了利用标称搜索树处理扰动以实现实时应用的有效性。