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The Mixed Fleet Vehicle Routing Problem with overtime and light loads Formulation and Population Variable Neighbourhood search with Adaptive Memory

Wassan, Niaz A., Salhi, Said, Wassan, Naveed A. (2017) The Mixed Fleet Vehicle Routing Problem with overtime and light loads Formulation and Population Variable Neighbourhood search with Adaptive Memory. In: 7th International Conference on Science, Management, Engineering and Technology 2017 (ICSMET 2017), 10-11 Jul 2017, Phuket, Thailand. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:65134)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
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http://icsmet.com/

Abstract

In this paper we consider a real-life Vehicle Routing Problem, characterized by heterogeneous vehicle fleet, demand-dependent service times, maximum allowable overtime and a special light load requirement. A new learning-based Population Variable Neighbourhood Search algorithm is designed to address this complex logistic problem. The computational experience suggests that savings up to 8% can be achieved when overtime and light load requirements are considered in advance. 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 incurring extra running costs. The proposed metaheuristic method also shows some competitive results when applied to the special case of the real-life Vehicle Routing Problem, namely the Fleet Size and Mix Vehicle Routing Problem.

Item Type: Conference or workshop item (Proceeding)
Uncontrolled keywords: Population Variable Neighbourhood Search; Adaptive Memory; Real Life Vehicle Routing; MIP Formulation; Metaheuristic
Divisions: Faculties > Social Sciences > Kent Business School
Faculties > Social Sciences > Kent Business School > Centre for Logistics and Heuristic Organisation (CLHO)
Depositing User: Niaz Wassan
Date Deposited: 12 Dec 2017 17:20 UTC
Last Modified: 08 Feb 2020 04:09 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/65134 (The current URI for this page, for reference purposes)
Wassan, Niaz A.: https://orcid.org/0000-0003-0153-7646
Salhi, Said: https://orcid.org/0000-0002-3384-5240
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