Solving the Feeder Vehicle Routing Problem using ant colony optimization
| Autor: | Guillermo Latorre-Núñez, Germán Paredes-Belmar, Carola Blazquez, Shan Huen Huang, Ying-Hua Huang |
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| Rok vydání: | 2019 |
| Předmět: |
Mathematical optimization
021103 operations research General Computer Science Computer science Heuristic (computer science) Heuristic Ant colony optimization algorithms 0211 other engineering and technologies General Engineering Process (computing) 02 engineering and technology Vehicle routing problem 0202 electrical engineering electronic engineering information engineering Benchmark (computing) 020201 artificial intelligence & image processing Metaheuristic |
| Zdroj: | Computers & Industrial Engineering. 127:520-535 |
| ISSN: | 0360-8352 |
| DOI: | 10.1016/j.cie.2018.10.037 |
| Popis: | This paper studies the Feeder Vehicle Routing Problem (FVRP), a new variant of the vehicle routing problem (VRP), in which each customer is served by either a large (truck) or a small vehicle (motorcycle). In this particular type of delivery, the trucks and the motorcycles must depart from the depot, visit the customers, and eventually return to the depot. During the delivery process, the motorcycles travel to the truck locations for reloading. The ant colony optimization (ACO) algorithm is employed for solving the problem with the objective of determining the number of dispatching sub-fleets and optimal routes to minimize the total cost (fixed route and travel costs). Three benchmark datasets are generated to examine the performance of the FVPR. For comparison purposes, all instances are executed by dispatching only trucks as in the traditional VRP and a four-stage hierarchical heuristic. Additionally, ACO is compared to optimal solutions for small instances. The results indicate that the proposed ACO algorithm yields promising solutions particularly for large instances within a reasonable time frame in an efficient manner. |
| Databáze: | OpenAIRE |
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