Abstract
The capacitated arc routing problem (CARP) is a challenging vehicle routing problem with numerous real-world applications. In this paper, an extended version of CARP, the multi-depot multi-objective capacitated arc routing problem (MDMOCARP) is proposed to tackle practical requirements. Firstly, the critical edge decision mechanism and the critical edge random allocation mechanism are proposed to optimize edges between depots. Secondly, a novel adaptive probability of local search with fitness is proposed to improve the Decomposition-Based Memetic Algorithm for Multi-Objective CARP (D-MAENS). Compared with the D-MAENS algorithm, experimental results on MD-CARP instances show that the improved memetic algorithm (IMA) has performed significantly better than D-MAENS on convergence and diversity in the metric IGD and the metric HV.
Highlights
Capacitated Arc Routing Problem (CARP) [1] has wide real-world applications such as snow removal [2], urban waste collection [3,4], pipeline repair [5] etc
In the multi-objective CARP (MOCARP), Lacomme et al [13] studied the two-objective capacitated arc routing problem model to meet the needs of Troyes in waste collection, and solved the problem by using NSGAII algorithm
The two performances metrics evaluate the performance of the two algorithms. one is the inverted generational distance from reference set (IGD) [17], which indicates the convergence and diversity of a solution set in multi-objective problem
Summary
Capacitated Arc Routing Problem (CARP) [1] has wide real-world applications such as snow removal [2], urban waste collection [3,4], pipeline repair [5] etc. Today, these practical issues that are closely related to the logistics transportation have become very important to the government administration. In the multi-objective CARP (MOCARP), Lacomme et al [13] studied the two-objective capacitated arc routing problem model to meet the needs of Troyes in waste collection, and solved the problem by using NSGAII algorithm.
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