Improved genetic algorithm for job shop scheduling

Abstract

In order to obtain the optimal solution of genetic algorithm for job shop scheduling problem and improve the iteration speed of the algorithm, the improved method of genetic algorithm is studied. The scheduling model is established with the shortest processing time of the workpiece as the target. An adaptive crossover and mutation operator based on probability improvement is proposed to get the optimal solution of the job shop scheduling problem. The elitist retention strategy and the improved adaptive operator are used in the genetic algorithm, to solve solve job shop scheduling problem. The benchmark cases LA01 and FT06 are used as simulation objects. The corresponding Gantt chart and the search process curve are obtained. The simulation results show that the improved algorithm can get the optimal solution more quickly with the unmodified algorithm. The improved algorithm is more efficient and faster. It is feasible to solve job shop scheduling problem, and is more suitable for industrial production.

Keywords

• optimization
• machinery workshop
• job shop scheduling
• adaptive operator
• elitist strategy
• improved genetic algorithm