The Optimization And Simulation On Shop Scheduling Problem Based On Improved Genetic Algorithm

Due to economic globalization and fierce competition in companies, optimizing shop scheduling of inner manufacturing management has played an important role. Now lots of theoretical investigation of shop scheduling has been done, however, most of them haven't been applied in practical production. This thesis has optimized the traditional Genetic Algorithm (GA) to solve Flow Shop Scheduling Problem (FSP), Hybrid Flow-shop Scheduling Problem (HFSP) and Job Shop Scheduling Problem (JSSP). Flexsim software is used to simulate these problems. The simulation results prove that applying Improved Genetic Algorithm (IGA) to solve the shop scheduling problem is feasible and robust.Firstly, the thesis has improved upon the structure of GA to a parallel structure of algorithm aiming at the problem that GA is easy to run into local optima, appear premature convergence and has low probability of convergence after research and analysis. And the IGA, including dynamic self-adapting method and hybrid heuristic method, can search the global optimum rather than local optimum, and enhance the optimum rate based on fast searching rate.Secondly, the thesis has solved FSP, HFSP and JSSP by different implementation methods of IGA and optimized the procedure. This algorithm has some better results by means of vefifying the problem of the benchmarks of shop scheduling problem, automobile engine model and locomotive model.Finally, the thesis has established IGA procedure, designed a visual programming interface, and achieved dynamic simulation of Flexsim on FSP, HFSP and JSSP. The whole scheduling process of dynamic simulation can be observed, which proves the IGA feasible and efficient in practice.IGA could improve the problem of low probability of convergence. Objective function could fulfill the need of client on the shop scheduling. The paper is of profound practical significance.