A Study on Job Shop Scheduling Problem Using a Genetic Algorithm in Production planning

Abstract

Job shop scheduling problem (JSSP) is a critical factor in smart manufacturing, which can provide a practical approach to arrange materials, resources properly under the constraints and requirements in real-life. In production planning, scheduling is one of the most important steps. Scheduling is a technique to determines when an operation is to be performed or when work is to be completed. The minimization of the make-span is the main goal of the job shop scheduling. To schedule the jobs there are some constraints for job processing in the job shop scheduling problem. The genetic algorithm purpose to job shop scheduling for schedule jobs and minimize the make-span. As a result, it will show the best optimal sequence, optimal value, elapsed time, and Gantt chart for jobs and machines. A genetic algorithm for job shop scheduling gets an accurate result. multi-objective scheduling problem. The non-dominated sorting algorithm being used for multi-objective scheduling problems. The architecture of NSGA-II is similar to GA. The only major difference steps are Combine parent and offspring population, Non-dominated sorting, Calculate crowding-distance, etc There are two goals, first is to minimize the total completion time (make-span) and the second is the total weighted early time and delay time (total weighted earliness and tardiness, TWET). Keyword-: Job shop scheduling, genetic algorithm, Nondominated sorting genetic algorithm, Make-span,