Abstract
The capacitated arc routing problem (CARP) is a challenging combinatorial optimisation problem abstracted from many real-world applications, such as waste collection, road gritting and mail delivery. However, few studies considered dynamic changes during the vehicles' service, which can cause the original schedule infeasible or obsolete. The few existing studies are limited by the dynamic scenarios considered, and by overly complicated algorithms that are unable to benefit from the wealth of contributions provided by the existing CARP literature. In this paper, we first provide a mathematical formulation of dynamic CARP (DCARP) and design a simulation system that is able to consider dynamic events while a routing solution is already partially executed. We then propose a novel framework which can benefit from existing static CARP optimisation algorithms so that they could be used to handle DCARP instances. The framework is very flexible. In response to a dynamic event, it can use either a simple restart strategy or a sequence transfer strategy that benefits from past optimisation experience. Empirical studies have been conducted on a wide range of DCARP instances to evaluate our proposed framework. The results show that the proposed framework significantly improves over state-of-the-art dynamic optimisation algorithms.
Highlights
The Capacitated Arc Routing Problem (CARP) is a classical combinatorial optimisation problem with a range of collection and delivery applications in the real world
We show that valuable research progress achieved by the static CARP literature can contribute towards optimisation results that significantly outperform the existing algorithm [16] that was designed for Dynamic CARP (DCARP)
We studied the dynamic capacitated arc routing problem (DCARP), in which dynamic events, such as road closure, added tasks, etc., occur during the vehicles’ service
Summary
The Capacitated Arc Routing Problem (CARP) is a classical combinatorial optimisation problem with a range of collection and delivery applications in the real world. Liu et al [17] proposed a benchmark generator for DCARP Their generator cannot consider dynamic events during the execution of a routing solution and, is unsuitable for our DCARP scenarios, where changes happen during the execution of the scenarios. There is a rich literature on existing CARP optimisation algorithms that could potentially contribute towards DCARP optimisation, but they are not applicable to DCARP instances This is because they work under the assumption that all vehicles start at the depot and have the same capacities, which is not the case in DCARP. We show that valuable research progress achieved by the static CARP literature can contribute towards optimisation results that significantly outperform the existing algorithm [16] that was designed for DCARP.
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