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

•VNS-triggered memory extraction improves method performance up to 5.2%.•Incorporating real life aspects could improve daily total routing cost up to 8%.•Vehicle capacity and working time utilization could be improved by up to 12.5%.•Real life aspects could improve fleet composition at no extra cost...

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Vydáno v:Expert systems with applications Ročník 114; s. 183 - 195
Hlavní autoři: Simeonova, Lina, Wassan, Niaz, Salhi, Said, Nagy, Gábor
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Elsevier Ltd 30.12.2018
Elsevier BV
Témata:
Adaptive Memory
Adaptive search techniques
Decision making
Expert systems
Iterative methods
Machine learning
Managerial Insights
Mathematical analysis
MIP Formulation
Motor vehicle fleets
Population Variable Neighbourhood Search
Real life vehicle routing
Route optimization
Route planning
Search algorithms
Transportation problem (Operations research)
Vehicle routing
Working hours
Real life vehicle routing
Managerial Insights
Adaptive Memory
Population Variable Neighbourhood Search
MIP Formulation
ISSN:0957-4174, 1873-6793
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Shrnutí:•VNS-triggered memory extraction improves method performance up to 5.2%.•Incorporating real life aspects could improve daily total routing cost up to 8%.•Vehicle capacity and working time utilization could be improved by up to 12.5%.•Real life aspects could improve fleet composition at no extra cost.•Interesting managerial insights regarding real life routing trade-offs. 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.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2018.07.034