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A new crossover to solve the full truckload vehicle routing problem using genetic algorithm
Titre | A new crossover to solve the full truckload vehicle routing problem using genetic algorithm |
Publication Type | Conference Paper |
Year of Publication | 2016 |
Authors | Bouyahyiouy, KEl, Bellabdaoui, A |
Editor | Alaoui, AE, Benadada, Y, Boukachour, J |
Conference Name | PROCEEDINGS OF THE 3RD IEEE INTERNATIONAL CONFERENCE ON LOGISTICS OPERATIONS MANAGEMENT (GOL'16) |
Publisher | Sidi Mohammed Ben Abdellah Univ Fes, Fac Sci & Technol; Mohammed V Univ Rabat, ENSIAS Sch; Univ Havre; IEEE |
ISBN Number | 978-1-4673-8571-8 |
Abstract | This paper considers the full-truckload selective multi-depot vehicle routing problem under time windows constraints (denoted by FT-SMDVRPTW), which is a generalization of the vehicle routing problem (VRP). Our objective function is to maximize the total profit that the vehicle generates during its trip. In this study, we'll present a review of literature about full truckload vehicle routing; we'll define the FT-SMDVRPTW that will be resolved via using genetic algorithm. A new complex two-part chromosome is used to represent the solution to our problem. Through a selection based on the elitist method and roulette method, an improved crossover operator called selected two-part chromosome crossover (STCX), and swap mutation operator new individuals are generated. Finally, we give a numerical example on a randomly generated instance to illustrate our approach.
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