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The heterogeneous fleet vehicle routing problem with light loads and overtime: Formulation and population variable neighbourhood search with adaptive memory

Simeonova, Lina, Wassan, Niaz, Salhi, Said, Nagy, Gábor (2018) The heterogeneous fleet vehicle routing problem with light loads and overtime: Formulation and population variable neighbourhood search with adaptive memory. Expert Systems with Applications, 114 . pp. 183-195. ISSN 0957-4174. (doi:10.1016/j.eswa.2018.07.034) (KAR id:68991)

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https://dx.doi.org/10.1016/j.eswa.2018.07.034

Abstract

In this paper we consider a real life Vehicle Routing Problem inspired by the gas delivery industry in the United Kingdom. The problem is characterized by heterogeneous vehicle fleet, demand-dependent service times, maximum allowable overtime and a special light load requirement. A mathematical formulation of the problem is developed and optimal solutions for small sized instances are found. A new learning-based Population Variable Neighbourhood Search algorithm is designed to address this real life logistic problem. To the best of our knowledge Adaptive Memory has not been hybridized with a classical iterative memoryless method. In this paper we devise and analyse empirically a new and effective hybridization search that considers both memory extraction and exploitation. In terms of practical implications, we show that on a daily basis up to 8% cost savings on average can be achieved when overtime and light load requirements are considered in the decision making process. Moreover, accommodating for allowable overtime has shown to yield 12% better average utilization of the driver's working hours and 12.5% better average utilization of the vehicle load, without a significant increase in running costs. We also further discuss some managerial insights and trade-offs.

Item Type: Article
DOI/Identification number: 10.1016/j.eswa.2018.07.034
Uncontrolled keywords: Real life vehicle routing, Population Variable Neighbourhood Search, Adaptive Memory, MIP Formulation, Managerial Insights
Subjects: H Social Sciences
Divisions: Faculties > Social Sciences > Kent Business School
Depositing User: Niaz Wassan
Date Deposited: 07 Sep 2018 15:53 UTC
Last Modified: 06 May 2020 03:18 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/68991 (The current URI for this page, for reference purposes)
Simeonova, Lina: https://orcid.org/0000-0003-4285-3610
Wassan, Niaz: https://orcid.org/0000-0003-0153-7646
Salhi, Said: https://orcid.org/0000-0002-3384-5240
Nagy, Gábor: https://orcid.org/0000-0002-7609-5718
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