Optimization of plant location problem in interval domain via particle swarm optimization

This paper explores the concept of plant location problem in interval domain. In many formulations of plant location problem, it is assumed that parameters relating to the problems are precise valued, which is the traditional way to formulate the problem. Here, we consider all the parameters of the problem are imprecise and impreciseness has been represented by interval numbers. Now days, set up of different industries/plants as well as logistic centers plays an important role for developing the nation’s economy and also significantly contribute to the economic and social development of specified regions in which they are located. So setup of different industries/plants/logistic centers is a crucial measure and ensuring that such important industries/plants/logistic centers are both efficient and productive. This type of problem falls under the problem of production planning and distribution problems. To achieve this goal, optimization technique plays a vital role. Recently, different types of optimization techniques have been advanced rapidly and applied widely to solve production planning and distribution problems. Some of these are Genetic Algorithm, Particle swarm Optimization, Differential Evolution, Ant Colony Optimization etc. and these advancements are supported both by continuous updating progress of computer technologies and that of the optimization process for large scale system. In this paper, we have considered and solved plant location problem which is a most important optimization problem. The main goal of plant location problem is to setup a plant/industry in such a place that for which the total cost of transportations is minimum. In existing literature plant location problems have always been addressed in the deterministic domain i.e., all the parameters relating to the problems are fixed/precise. However, due to uncertainty/impreciseness, here we have presented the plant location problem in interval environment. New formulations in interval environment and solution methods based on Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) have been developed. Finally, a numerical example has been considered and solved for illustration purpose.