Multi-ant colony system (MACS) for a vehicle routing problem with backhauls

• Published in: European journal of operational research Vol. 196; no. 1; pp. 102 - 117
• Main Authors: Gajpal, Yuvraj, Abad, P.L.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01-07-2009; Elsevier; Elsevier Sequoia S.A
• Series: European Journal of Operational Research
• Subjects: Ant colony; Applied sciences; Exact sciences and technology; Flows in networks. Combinatorial problems; Logistics; Metaheuristic; Metaheuristic Ant colony Vehicle routing; Operational research and scientific management; Operational research. Management science; Optimization algorithms; Route optimization; Studies; Vehicle routing; Vehicles; Ant colony; Vehicle routing; Metaheuristic; Local search; Travel time; Vehicle routing problem; Algorithmics; Pheromone; Routing; Social insect; Combinatorial optimization; Foraging behavior; Particle swarm optimization; Delivery (good); Heuristic method; Capacity constraint; Swarm intelligence; Metamodel
• ISSN: 0377-2217; 1872-6860
• DOI: 10.1016/j.ejor.2008.02.025

The vehicle routing problem with backhaul (VRPB) is an extension of the capacitated vehicle routing problem (CVRP). In VRPB, there are linehaul as well as backhaul customers. The number of vehicles is considered to be fixed and deliveries for linehaul customers must be made before any pickups from backhaul customers. The objective is to design routes for the vehicles so that the total distance traveled is minimized. We use multi-ant colony system (MACS) to solve VRPB which is a combinatorial optimization problem. Ant colony system (ACS) is an algorithmic approach inspired by foraging behavior of real ants. Artificial ants are used to construct a solution by using pheromone information from previously generated solutions. The proposed MACS algorithm uses a new construction rule as well as two multi-route local search schemes. An extensive numerical experiment is performed on benchmark problems available in the literature.