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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| Vydané v: | Expert systems with applications Ročník 114; s. 183 - 195 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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New York
Elsevier Ltd
30.12.2018
Elsevier BV |
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| ISSN: | 0957-4174, 1873-6793 |
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| Abstract | •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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| AbstractList | •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. 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. |
| Author | Wassan, Niaz Simeonova, Lina Nagy, Gábor Salhi, Said |
| Author_xml | – sequence: 1 givenname: Lina surname: Simeonova fullname: Simeonova, Lina email: ls444@kentforlife.net – sequence: 2 givenname: Niaz surname: Wassan fullname: Wassan, Niaz email: N.A.Wassan@kent.ac.uk – sequence: 3 givenname: Said orcidid: 0000-0002-3384-5240 surname: Salhi fullname: Salhi, Said email: S.Salhi@kent.ac.uk – sequence: 4 givenname: Gábor surname: Nagy fullname: Nagy, Gábor email: G.Nagy@kent.ac.uk |
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| Snippet | •VNS-triggered memory extraction improves method performance up to 5.2%.•Incorporating real life aspects could improve daily total routing cost up to... 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... |
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| SubjectTerms | 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 |
| Title | The heterogeneous fleet vehicle routing problem with light loads and overtime: Formulation and population variable neighbourhood search with adaptive memory |
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