A coevolutionary algorithm for the flexible delivery and pickup problem with time windows

This paper addresses a flexible delivery and pickup problem with time windows (FDPPTW) and formulates the problem into a mixed binary integer programming model in order to minimize the number of vehicles and to minimize the total traveling distance. This problem is shown to be NP-hard. In this study...

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Bibliographic Details
Published in:International journal of production economics Vol. 141; no. 1; pp. 4 - 13
Main Authors: Wang, Hsiao-Fan, Chen, Ying-Yen
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 01.01.2013
Elsevier Sequoia S.A
Subjects:
Algorithms
Benchmarking
Bi-directional logistics
Coevolutionary algorithm
Deliveries
Delivery and pickup
Economics
Effectiveness studies
Genetic algorithms
Insertion
Integer programming
Mathematical models
Route optimization
Vehicle routing problem with backhaul
Vehicles
Windows (intervals)
Coevolutionary algorithm
Delivery and pickup
Bi-directional logistics
Vehicle routing problem with backhaul
ISSN:0925-5273, 1873-7579
Online Access:Get full text
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Summary:This paper addresses a flexible delivery and pickup problem with time windows (FDPPTW) and formulates the problem into a mixed binary integer programming model in order to minimize the number of vehicles and to minimize the total traveling distance. This problem is shown to be NP-hard. In this study, therefore, a coevolutionary algorithm incorporated with a variant of the cheapest insertion method is developed to speed up the solution procedure. The FDPPTW scheme overcomes the shortcomings of the existing schemes for the delivery and pickup problems. By testing with some revised Solomon's benchmark problems, the computational results have shown the efficiency and the effectiveness of the developed algorithm. ► Both logistic services of delivery and pickup can be performed according to the customers' needs. ► Used products are recycled by the reverse logistic. ► Customers can request the services within certain time windows. ► A heuristic genetic algorithm is developed based on co-populations. ► Evaluation of efficiency and accuracy are conducted in comparative manner.
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ISSN:0925-5273
1873-7579
DOI:10.1016/j.ijpe.2012.04.011