Solving the Feeder Vehicle Routing Problem using ant colony optimization

Ying Hua Huang, Carola A. Blazquez, Shan Huen Huang, Germán Paredes-Belmar, Guillermo Latorre-Nuñez

Resultado de la investigación: Article

1 Cita (Scopus)

Resumen

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.

Idioma originalEnglish
PublicaciónComputers and Industrial Engineering
DOI
EstadoAccepted/In press - 1 ene 2018

Huella dactilar

Vehicle routing
Ant colony optimization
Trucks
Motorcycles
Costs

ASJC Scopus subject areas

  • Computer Science(all)
  • Engineering(all)

Citar esto

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title = "Solving the Feeder Vehicle Routing Problem using ant colony optimization",
abstract = "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.",
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author = "Huang, {Ying Hua} and Blazquez, {Carola A.} and Huang, {Shan Huen} and Germ{\'a}n Paredes-Belmar and Guillermo Latorre-Nu{\~n}ez",
year = "2018",
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Solving the Feeder Vehicle Routing Problem using ant colony optimization. / Huang, Ying Hua; Blazquez, Carola A.; Huang, Shan Huen; Paredes-Belmar, Germán; Latorre-Nuñez, Guillermo.

En: Computers and Industrial Engineering, 01.01.2018.

Resultado de la investigación: Article

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T1 - Solving the Feeder Vehicle Routing Problem using ant colony optimization

AU - Huang, Ying Hua

AU - Blazquez, Carola A.

AU - Huang, Shan Huen

AU - Paredes-Belmar, Germán

AU - Latorre-Nuñez, Guillermo

PY - 2018/1/1

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N2 - 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.

AB - 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.

KW - Ant colony optimization

KW - Heterogeneous vehicles

KW - Metaheuristics

KW - Vehicle routing problem

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