ANALYSIS OF SELECTION SCHEMES FOR SOLVING JOB SHOP SCHEDULING PROBLEM USING GENETIC ALGORITHM

Abstract

Scheduling problems have the standard consideration in the field of manufacturing. Among the various t ypes of scheduling problems, the job shop scheduling problem is one of the most interesting NP-hard problems. As the job shop scheduling is an optimization problem, Genetic algorithm was selecte d to solve it In this study. Selection scheme is one of the important operators of Genetic algorithm. The choice of selection metho d to be applied for solving problems has a wide rol e in the Genetic algorithm process. The speed of convergence towards the optim um solution for the chosen problem is largely deter mined by the selection mechanism used in the Genetic algorithm. Depending upon the selection scheme applied, the population f itness over the successive generations could be improved. There are various type of selection schemes in genetic algor ithm are available, where each selection scheme has its own feasibility for sa particular problem. In this study, the se lection schemes namely Stochastic Universal Sampling (SUS), Roulette Wheel Selection (RWS), Rank Based Roulette Wheel Selecti on (RRWS) and Binary Tournament Selection (BTS) were chosen for implemen tation. The characteristics of chosen selection mec hanisms of Genetic algorithm for solving the job shop scheduling probl em were analyzed. The Genetic algorithm with four d ifferent selection schemes is tested on instances of 7 benchmark problems of d ifferent size. The result shows that the each of the four selection schemes of Genetic algorithm have been successfully applied to the job shop scheduling problems efficiently and t he performance of Stochastic Universal Sampling selection method is b etter than all other four selection schemes.