Publication Type : Journal Article
Publisher : International Journal of Artificial Intelligent Systems and Machine Learning
Source : International Journal of Artificial Intelligent Systems and Machine Learning, Volume 3, Issue 11 (2011)
Url : http://www.ciitresearch.org/dl/index.php/aiml/article/view/AIML102011002
Keywords : Clarke and Wright Saving Method, MDVRP, Optimal Route, particle swarm optimization, Scheduling
Campus : Bengaluru
School : School of Engineering
Department : Electrical and Electronics
Year : 2011
Abstract : The Multi-Depot Vehicle Routing Problem (MDVRP), an extension of classical VRP, is a NP-hard problem for simultaneously determining the routes for several vehicles from multiple depots to a set of customers and then return to the same depot. The objective of the problem is to find routes for vehicles to service all the customers at a minimal cost in terms of number of routes and total travel distance, without violating the capacity and travel time constraints of the vehicles. The solution to the MDVRP, in this paper, is obtained through Particle Swarm Optimization (PSO). The customers are grouped based on distance to their nearest depots and then routed with Clarke and Wright saving method. Further the routes are scheduled and optimized using POS. A set of five different Cordeau’s benchmark instances (p01, p02, p03, p04, p06) from the online resource of University of Malaga, Spain were experimented using MATLAB R2008b software. The results were evaluated in terms of depot’s route length, optimal route, optimal distance, computational time, average distance, and number of vehicles. Comparison of the experimental results with state-of-the-art techniques shows that the performance of PSO is feasible and effective for solving the multi-depot vehicle routing problem.
Cite this Research Publication : P. Surekha and Sumathi, S., “Application of Particle Swarm Optimization for Solving Multi-Depot Vehicle Routing Problems”, International Journal of Artificial Intelligent Systems and Machine Learning, vol. 3, no. 11, 2011.