Genetic Algorithm for Solving Job-Shop Scheduling Problem

Job-shop scheduling problem is becoming more and more prominent multi objective in the modern production. For solving target of Job-shop, genetic algorithm encoding was improved and fitness function contained the average process time and all the workpiece completion time in this paper. At the same time, self-adaptive crossover and self-adaptive mutation was taken. Experiments show that the algorithm can provide good production plan for small-scale manufacturing enterprises, and ensure production schedules while accelerating the pace of capital flows.