Abstract—Vehicle Routing Problem (VRP) is a NP Complete and a multi-objective problem. The problem involves optimizing a fleet of vehicles that are to serve a number of customers from a central depot. Each vehicle has limited capacity and each customer has a certain demand. Genetic Algorithm (GA) maintains a population of solutions by means of a crossover and mutation operators. For crossover and mutation best cost route crossover techniques and swap mutation procedure is used respectively. In this paper, we focus on two objectives of VRP i.e. number of vehicles and total cost (distance). The proposed Multi Objective Genetic Algorithm (MOGA) finds optimum solutions effectively.
Index Terms—Vehicle routing problem, genetic algorithm, multi-objective optimization, pare to ranking procedure, best cost route crossover (BCRC).
The authors are with School of Computer Application, KIIT University, Bhubaneswar, India (e-mail: padma024@gmail.com, jnyana1@gmail.com).
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Cite:Padmabati Chand and J. R. Mohanty, "Multi Objective Genetic Approach for Solving Vehicle Routing Problem," International Journal of Computer Theory and Engineering vol. 5, no. 6, pp. 846-849, 2013.
