Simulation and Optimization of Job Shop Scheduling Problem using Genetic Algorithm: A Case Study

Job shop scheduling is an important step in planning and manufacturing control, and at the same time remain one of the well-known optimization problem. In a job shop scheduling problem, a set of jobs has to be processed on a set of machines such that a specific optimization criterion is satisfied. As job shop scheduling highly affects the efficiency of manufacture, a large number of optimization techniques have been applied to the problem to achieve a satisfied solution over the years. Here, an effort has been made to optimize a real world scheduling problem using a well-known technique genetic algorithm. Genetic algorithms (GAs) are a very popular heuristic which have been successfully applied to many optimization problems within the last 30 years. GAs are stochastic global search methods that mimic the natural biological evolution [1]. GAs operates on a population of possible solutions applying the principle of survival of the fittest to produce better and better approximations to a solution. In this work few conventional job shop scheduling problems are first solved using Genetic Algorithm and results are compared for validation. Then GA is applied to real world job shop scheduling problem. Few assumptions are made to the real world industry data to reduce the constraints. A model of the problem is developed in MATLAB platform and results are plotted, analyzed and discussed.