The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON COMMUNICATIONS
Transactions ID Number: 53-560
Full Name: Geetha Shanmugam
Position: Assistant Professor
Age: ON
Sex: Female
Address: Department of Mathematics and Computer Applications, PSG College of Technology
Country: INDIA
Tel: 9842692184
Tel prefix: +91
Fax: 2573833
E-mail address: geet_shan@yahoo.com
Other E-mails: sgeetha_viji@gmail.com
Title of the Paper: Nested Particle Swarm Optimization for Multi Depot Vehicle Routing Problem
Authors as they appear in the Paper: S. Geetha, G. Poonthalir, P. T. Vanathi
Email addresses of all the authors: geet_shan@yahoo.com, thalirkathir@rediffmail.com, ptvani@yahoo.com
Number of paper pages: 11
Abstract: Distribution logistics comprise of all activities related to the provision of finished products and merchandise to a customer. The focal point of distribution logistics is the shipment of goods from the manufacturer to the consumer. The products can either be delivered directly from the production process or from the trader's stock located close to the production site or, probably, via additional regional distribution warehouses to a customer. This kind of distribution logistics are mathematically represented as Vehicle Routing Problem (VRP), a well known NP-hard problem of Operations Research. VRP is more suited for applications having one warehouse. But in reality, companies as well as industries poses more than one distribution warehouse. These kinds of problem can be solved with an extension of VRP called as Multi-Depot VRP (MDVRP), it is a NP-hard and Combinatorial Optimization problem. MDVRP is an important and challenging problem in logistics management. In !
this work, the MDVRP is solved using Nested Particle Swarm Optimization (NPSO) with genetic operators. Cluster first and route second is the methodology used for solving MDVRP. The k-means algorithm with priority is used for clustering the customers to the nearest depot using minimax principle. First fit decreasing algorithm is used for allocating customers to the depot i.e., based on their demands. Thus MDVRP is reduced to multiple VRP. Multiple vehicle routes are formed within each cluster/depot. This work concentrates in using nested PSO for framing the vehicle routes. The PSO is first applied for assigning the customers to the vehicle within the depot/cluster called as master PSO. The PSO that takes care of forming the routes among the customers assigned to a vehicle is called as slave PSO. The mutation and crossover are the operators used with slave PSO. Nearest Neighbour Heuristic (NNH) is used to form initial best particles of slave PSO and 2-opt is used to perform l!
ocal search. The objective of MDVRP is to minimize the total travel le
ngth along with route and load balance among the depots and vehicles. MDVRP is considered with more than one objective in order to bring fairness into the play. The home delivery pharmacy program and waste collection problem are considered as case studies in this paper. The algorithm is implemented using MATLAB 7.0.1 and compared with benchmark data sets. The results obtained are better in balancing load, route length and the number of vehicles, rather than minimization of total cost.
Keywords: Nested Particle swarm optimization; Genetic operator; Local exchange; Multi-Depot Vehicle Routing Problem; Home delivery pharmacy program; Waste collection management.
EXTENSION of the file: .pdf
Special (Invited) Session:
Organizer of the Session:
How Did you learn about congress: Soft computing, Modern routing problem
IP ADDRESS: 117.197.202.245
