The Multiple Trip Vehicle Routing Problem with Backhauls: Formulation and a Two-Level Variable Neighbourhood Search

In this paper a new VRP variant the Multiple Trip Vehicle Routing Problem with Backhauls (MT-VRPB) is investigated. The classical MT-VRP model is extended by including the backhauling aspect. An ILP formulation of the MT-VRPB is first presented and CPLEX results for small and medium size instances a...

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Bibliographic Details
Published in:Computers & operations research Vol. 78; pp. 454 - 467
Main Authors: Wassan, Naveed, Wassan, Niaz, Nagy, Gábor, Salhi, Saïd
Format: Journal Article
Language:English
Published: New York Elsevier Ltd 01.02.2017
Pergamon Press Inc
Subjects:
Algorithms
Backhauling
Heuristic
Meta-heuristics
Multiple trips
Routing
Studies
VNS
Routing
VNS
Backhauling
Multiple trips
Meta-heuristics
ISSN:0305-0548, 1873-765X, 0305-0548
Online Access:Get full text
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Summary:In this paper a new VRP variant the Multiple Trip Vehicle Routing Problem with Backhauls (MT-VRPB) is investigated. The classical MT-VRP model is extended by including the backhauling aspect. An ILP formulation of the MT-VRPB is first presented and CPLEX results for small and medium size instances are reported. For large instances of the MT-VRPB a Two-Level VNS algorithm is developed. To gain a continuous balanced intensification and diversification during the search process VNS is embedded with the sequential VND and a multi-layer local search approach. The algorithm is tested on a set of new MT-VRPB data instances which we generated. Interesting computational results are presented. The Two-Level VNS produced excellent results when tested on the special variant of the VRPB. •An introduction of a new VRP variant – MT-VRPB – an ILP formation of the problem.•CPLEX implementation to test the model and producing optimal solutions and bounds.•A sweep-first-assignment-second that produces initial feasible solution quickly.•A Two-Level VNS algorithm to solve the MT-VRPB efficiently.•A new MT-VRPB data set is generated that could be used for benchmarking.•The developed algorithm is useable for the classical VRPB.
Bibliography:SourceType-Scholarly Journals-1
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content type line 14
ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2015.12.017