A Job-shop Scheduling Problem (jssp) Using Genetic Algorithm (ga)

The job-shop scheduling (JSS) is a schedule planning for low volume systems with many variations in requirements. In job-shop scheduling problem (JSSP) environment, there are j jobs to be processed on m machines with a certain objective function to be minimized. JSSP with j jobs to be processed on more than two machines have been classified as a combinatorial problem. They cannot be formulated as a linear programming and no simple rules or algorithms yield to optimal solutions in a short time. In this paper we used genetic algorithm (GA) with some modifications to deal with problem of job shop scheduling. GA once proposed by John Holland is a stochastic search technique based on Darwin’s principle of the survival of the strongest. In this paper, we generated an initial population randomly including the result obtain by some well known priority rules such as shortest processing time and longest processing time. From there, the population will go through the process of reproduction, crossover and mutation to create a new population for the next generation until some stopping criteria defined were reached. In this paper, we used the number of generations as a stopping criteria. In crossover and mutation, we used the critical block neighbourhood and the distance measured to help us evaluate the schedules. Result has shown that the implementation of critical block neighbourhood and the distance measure can lead us to the same result obtain by other methods.