TY - JOUR
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
N1 - Publisher Copyright:
© 2018 Elsevier Ltd
PY - 2019/1
Y1 - 2019/1
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
UR - http://www.scopus.com/inward/record.url?scp=85055552792&partnerID=8YFLogxK
U2 - 10.1016/j.cie.2018.10.037
DO - 10.1016/j.cie.2018.10.037
M3 - Article
AN - SCOPUS:85055552792
SN - 0360-8352
VL - 127
SP - 520
EP - 535
JO - Computers and Industrial Engineering
JF - Computers and Industrial Engineering
ER -