Model Analysis on Job Shop Scheduling in Automobile Industry using Ant Colony Optimization and Particle Swarm Optimization

摘要

Job Shop Scheduling is an optimization problem and is considered to be one of the most daunting combinatorial problems. It can be used to maximize the productivity in many industries, particularly in the automobile industry. There are two finite sets involved in this problem, one for the number of machines and the other for the number of jobs which each machine has to do. The real challenge is to find out the most efficient way to complete these tasks. This problem remains one of the most discussed problems, with researchers from all over the world discovering new and different methods to solve it. A plethora of methods and algorithms, including different types of queuing algorithms and even some genetic algorithms have been used to solve this problem. The practicality of the problem further makes it interesting and the computer science community is motivated to make the solution even more efficient. In this paper, we have used Ant Colony Optimization and Particle Swarm Optimization, techniques which are probabilistic and iterative respectively to solve the problem. The tool used for this purpose is MATLAB. After tabulating and visualizing the results, it is found that the Particle Swarm Optimization is much more efficient than the Ant Colony Optimization method. The processing time of the Ant Colony Optimization is approximately four times more than that of the Particle Swarm Optimization.