A hybridisation of adaptive variable neighbourhood search and large neighbourhood search: Application to the vehicle routing problem

Sze, Jeeu Fong and Salhi, Said and Wassan, Niaz A. (2016) A hybridisation of adaptive variable neighbourhood search and large neighbourhood search: Application to the vehicle routing problem. Expert Systems with Applications, 65 . pp. 383-397. ISSN 0957-4174. (doi:https://doi.org/10.1016/j.eswa.2016.08.060) (Full text available)

Abstract

In this paper, an adaptive variable neighbourhood search (AVNS) algorithm that incorporates large neighbourhood search (LNS) as a diversification strategy is proposed and applied to the capacitated vehicle routing problem. The AVNS consists of two stages: a learning phase and a multi-level VNS with guided local search. The adaptive aspect is integrated in the local search where a set of highly successful local searches is selected based on the intelligent selection mechanism. In addition, the hybridisation of LNS with the AVNS enables the solution to escape from the local minimum effectively. To make the algorithm more competitive in terms of the computing time, a simple and flexible data structure and a neighbourhood reduction scheme are embedded. Finally, we adapt a new local search move and an effective removal strategy for the LNS. The proposed AVNS was tested on the benchmark data sets from the literature and produced very competitive results.

Item Type:	Article
Uncontrolled keywords:	adaptive search, variable neighbourhood, large neighbourhood, data structure, neighbourhood reduction, hybridisation
Subjects:	Q Science > Operations Research - Theory
Divisions:	Faculties > Social Sciences > Kent Business School > Management Science Faculties > Social Sciences > Kent Business School > Centre for Logistics and Heuristic Organisation (CLHO)
Depositing User:	Said Salhi
Date Deposited:	12 Sep 2016 13:53 UTC
Last Modified:	07 Aug 2018 15:03 UTC
Resource URI:	https://kar.kent.ac.uk/id/eprint/57232 (The current URI for this page, for reference purposes)