A genetic algorithm for the simultaneous delivery and pickup problems with time window

► A vehicle routing problem with simultaneous delivery and pickup problems with time windows is considered. ► A mixed binary integer programming model denoted by SDPPTW is formulated. ► A co-evolution genetic algorithm is developed for solution. ► Evaluation of accuracy and efficiency is done. This...

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Bibliographic Details
Published in:Computers & industrial engineering Vol. 62; no. 1; pp. 84 - 95
Main Authors: Wang, Hsiao-Fan, Chen, Ying-Yen
Format: Journal Article
Language:English
Published: New York Elsevier Ltd 01.02.2012
Pergamon Press Inc
Subjects:
Benchmarks
Bi-directional logistics
Co-evolution genetic algorithm
Computer programs
Deliveries
Genetic algorithms
Insertion
Integer programming
Mathematical models
Simultaneous delivery and pickup
Software
Studies
Transportation problem (Operations research)
Vehicle routing problem with time windows
Windows (intervals)
Vehicle routing problem with time windows
Bi-directional logistics
Co-evolution genetic algorithm
Simultaneous delivery and pickup
ISSN:0360-8352, 1879-0550
Online Access:Get full text
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Summary:► A vehicle routing problem with simultaneous delivery and pickup problems with time windows is considered. ► A mixed binary integer programming model denoted by SDPPTW is formulated. ► A co-evolution genetic algorithm is developed for solution. ► Evaluation of accuracy and efficiency is done. This paper concerns a Simultaneous Delivery and Pickup Problem with Time Windows (SDPPTW). A mixed binary integer programming model was developed for the problem and was validated. Due to its NP nature, a co-evolution genetic algorithm with variants of the cheapest insertion method was proposed to speed up the solution procedure. Since there were no existing benchmarks, this study generated some test problems which revised from the well-known Solomon’s benchmark for Vehicle Routing Problem with Time Windows (VRPTW). From the comparison with the results of Cplex software and the basic genetic algorithm, the proposed algorithm showed that it can provide better solutions within a comparatively shorter period of time.
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ISSN:0360-8352
1879-0550
DOI:10.1016/j.cie.2011.08.018