Optimization of Job Shop Scheduling Using Genetic Algorithm
The Job-Shop Scheduling Problem (JSSP) is considered as one of the difficult combinatorial optimization problems and treated as a member of Non Polynomial problem class. The present work aims to develop a genetic algorithm based approach to solve the Job Shop scheduling problem. Operation based representation is used to decode the schedule in the algorithm. Two point crossover and flip inverse mutation is used in this algorithm. The algorithm is encoded and developed in MATLAB Software.
The input parameters are operation time and operation sequence for each job in the machines provided. The Objective of this present work is to minimize the makespan. The experimental results show that the proposed GA, as compared to earlier GA, not only improves the quality of solutions but also reduces the overall computational time.