A Novel Optimization Algorithm for Job-Shop Production Scheduling

With the intensification of global market competition and the increasing diversification of customer needs, production scheduling has been regarded as a very important approach for enterprise to achieve high efficiency, flexibility and reliability. In order to support application of smart production, an optimization algorithm was developed in this paper with a novel and generic optimization model with common coding for job-shop scheduling. In this paper, the job-shop scheduling model was realized by an improved genetic algorithm which uses a genetic coding for scheduling of jobs and shop distribution. To verify the develop model, benchmark dataset and Tabu Search (TS), Simulated Annealing (SA) and the general Genetic Algorithms (GA) algorithms were employed to make comparison. Through the dataset analysis with 14 case instances, comparing with TS, SA and general GA algorithms, the improved model in this paper was verified to reduce the completion time for jobs in almost instances and achieved results to reach LB for 4 instances of 14 ones. According to the convergence time record, the improved model was indicated to get best performance of result with good convergence feature.