Research On Job Shop Scheduling Problem Based On Improved Genetic Algorithm

Under the background of the rapid development of science and technology today,the intelligent manufacturing industry has gradually developed into the pillar of the national economy,replacing most of human mental and physical labor in industrial engineering.Scheduling is one of the important parts in intelligent manufacturing industry,which can bring high efficiency,low energy consumption and low cost benefits to manufacturing industry.In today's industrial engineering,the optimization research of workshop scheduling problem has become a hot spot and has very important value and significance.This article first introduces the research background,the characteristics of workshop scheduling and the current research situation at home and abroad,and then describes the theory and implementation technology of genetic algorithm.This paper studies JSP?FJSP and FJSP multi-objective respectively.The main research contents are as follows:(1)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 is used in the genetic algorithm,to 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(2)It is also to establish flexible workshop scheduling model with the aim of the shortest processing time.A method based on the combination of process coding and machine coding is adopted in the coding of the algorithm,and a fast decoding method is proposed for decoding.The tournament selection method is used in the selection operation,and the elite solution retention strategy is combined to prevent the better solution from being destroyed.The population initialization is designed in two different ways to generate a double population mechanism.The benchmark case mk01-10 is used to simulate theexperiment and obtain the problem solution.The experimental results are close to or reach the known optimal solution,which proves that the improved algorithm has good problem-solving ability.(3)In the actual flexible job shop scheduling,it is more practical to consider the research of reducing cost and energy consumption.Aiming at this problem,a flexible job shop scheduling with processing time,cost and energy consumption is established.The genetic algorithm is improved and the problem is optimized.This paper proposes an extended coding method based on process coding,which combines roulette with elite retention strategy to improve the efficiency and quality of solution.The method of weight method is used to transform multiple targets into single targets,which reduces the difficulty of the algorithm.Local search algorithm is incorporated into the algorithm to improve the optimal solution of scheduling problem.Two groups of experiments are used to simulate the improved genetic algorithm.The experimental results show that the improved genetic algorithm is efficient and feasible.