Due to its direct relevance to post-disaster operations, meter reading and civil refuse collection, the Uncertain Capacitated Arc Routing Problem (UCARP) is an important optimisation problem. Stochastic models are critical to study as they more accurately represent the real world than their deterministic counterparts. Although there have been extensive studies in solving routing problems under uncertainty, very few have considered UCARP, and none consider collaboration between vehicles to handle the negative effects of uncertainty. This article proposes a novel Solution Construction Procedure (SCP) that generates solutions to UCARP within a collaborative, multi-vehicle framework. It consists of two types of collaborative activities: one when a vehicle unexpectedly expends capacity (route failure), and the other during the refill process. Then, we propose a Genetic Programming Hyper-Heuristic (GPHH) algorithm to evolve the routing policy used within the collaborative framework. The experimental studies show that the new heuristic with vehicle collaboration and GP-evolved routing policy significantly outperforms the compared state-of-the-art algorithms on commonly studied test problems. This is shown to be especially true on instances with larger numbers of tasks and vehicles. This clearly shows the advantage of vehicle collaboration in handling the uncertain environment, and the effectiveness of the newly proposed algorithm.

中文翻译：

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车辆协作的遗传编程超启发式算法解决不确定的电容式电弧路由问题

由于与灾后操作、抄表和民用垃圾收集直接相关，不确定电容弧路由问题 (UCARP) 是一个重要的优化问题。随机模型对于研究至关重要，因为它们比确定性模型更准确地代表了现实世界。尽管在解决不确定性下的路径问题方面已经有广泛的研究，但很少有人考虑过 UCARP，也没有人考虑车辆之间的协作来处理不确定性的负面影响。本文提出了一种新颖的解决方案构建程序 (SCP)，可在协作的多车辆框架内为 UCARP 生成解决方案。它包括两种类型的协作活动：一种是在车辆意外消耗容量（路线故障）时，另一种是在重新填充过程中。然后，我们提出了一种遗传编程超启发式 (GPHH) 算法来改进协作框架内使用的路由策略。实验研究表明，车辆协作和 GP 进化的路由策略的新启发式算法在常见的测试问题上明显优于比较的最先进算法。在具有大量任务和车辆的情况下尤其如此。这清楚地表明了车辆协作在处理不确定环境方面的优势，以及新提出的算法的有效性。实验研究表明，车辆协作和 GP 进化的路由策略的新启发式算法在常见的测试问题上明显优于比较的最先进算法。在具有大量任务和车辆的情况下尤其如此。这清楚地表明了车辆协作在处理不确定环境方面的优势，以及新提出的算法的有效性。实验研究表明，车辆协作和 GP 进化的路由策略的新启发式算法在常见的测试问题上明显优于比较的最先进算法。在具有大量任务和车辆的情况下尤其如此。这清楚地表明了车辆协作在处理不确定环境方面的优势，以及新提出的算法的有效性。