An Improved Multi-objective Genetic Algorithm In The Job Shop Scheduling Research

At present, the manufacturing sector, increasing competition, in the ordinary course of business, would often encounter a wide variety of complex scheduling problems, shop scheduling problem-solving will have a direct impact on the operational efficiency of enterprises and end-customers satisfaction and ultimately affect the business-to-market response ability and competitiveness. Therefore, the scheduling problem has become a major field of operations management research focus.The traditional multi-objective genetic algorithm for solving job shop scheduling problem in slow convergence rate and easily into local optimization, the paper proposes a kind of cross-point method using multi-objective genetic algorithms. Multi-point crossover operator initial approach to improve convergence speed. Gradually reduced in the latter part of the intersection of computing, until cross with two, single-point crossover approach, to avoid missing the optimal solution leading to premature convergence. At the same time the weight will design an interactive multi-objective problem into single objective problem, reflect the decision maker preferences, while simplifying the solution process. Finally, the improved algorithm applied to job shop scheduling problem, with no preference for multi-objective optimization Pareto genetic algorithm niche (NPGA) were compared, the results show the effectiveness of the algorithm.The improved multi-objective genetic algorithm for job shop scheduling system can be more efficient for a variety of manufacturers, to provide a different scale of the problem solving program.