Research On Flexible Job-shop Scheduling Problem Based On Improved Particle Swarm Optimization Algorithm

Manufacturing industry is an important basis to support the development of modern industrial technology.With the implementation of China’s ’going out’ grand strategy,a large number of domestic enterprises are also actively looking for a new foothold.In the process of manufacturing enterprises,the solution of resource scheduling problem will directly affect the operation efficiency of enterprises and the final customer satisfaction.In the actual production scheduling,the purpose of FJSP is to select reasonable processing machines and processing sequence for the process,so as to improve the production efficiency of the workshop and reduce the loss of machinery and equipment.Compared with traditional job shop scheduling problem,the flexible job shop scheduling problem increases the selectivity of the workpiece to the machine,that is,the workpiece can be processed by one machine among many machinable machines,which is more in line with the actual state of the workshop.In this paper,the improved particle swarm optimization algorithm is adopted to solve the flexible job shop scheduling problem considering the transportation of the workpiece and adding the automated guided vehicle.The specific work is as follows:Firstly,by summarizing the research situation of flexible shop scheduling problem at home and abroad,the deficiencies in the current research on such problems are analyzed,and the starting point of this topic is found.Particle swarm optimization algorithm is used to solve this kind of optimization problems.Secondly,the particle swarm optimization algorithm(PSO)is improved for solving single objective and multi-objective optimization problems.In this paper,neighborhood search algorithm and competitive learning mechanism are introduced into discrete particle swarm optimization(PSO)to improve the local search ability and global search ability of PSO respectively.Aiming at the problem of multi-objective flexible job shop scheduling,in order to ensure that the solution set can converge and distribute evenly in the solution space,the particle swarm optimization(PSO)was combined with the niche technology to maintain the diversity of the population in the optimization process,and the convergence of the algorithm was realized by controlling the number of small and medium habitats in the optimization process.The feasibility and effectiveness of the two improved algorithms in solving single objective and multi-objective flexible job shop scheduling problems are verified by simulation experiments respectively.Finally,the flexible job shop scheduling problem under complex constraints is studied.A dynamic initialization method was designed to solve the flexible job-shop scheduling problem considering the transportation time of the workpiece.In the initialization stage,the transportation time and processing time of the workpiece were considered,and the machine with the shortest completion time was selected for each process to improve the quality of the initial solution and thus accelerate the convergence speed of the algorithm.Flexible Job-shop Scheduling with automated guided vehicle(AGV)is an optimization problem with double resource constraints,which considers the transport resources in the transport stage of the workpiece.To solve this problem,a mathematical model with double resource constraints is established,and a heuristic initialization method is designed to improve the quality of the initial population solution.Through simulation experiments,the improved particle swarm optimization algorithm is compared with other optimization algorithms to solve the flexible job shop scheduling problem with transport time and AGV.It shows that the improved particle swarm optimization algorithm in this paper is feasible and efficient in solving these two kinds of problems.