Research On Shop Scheduling Based On Improved Particle Swarm And Genetic Hybrid Algorithm

As an important part of the production process management of enterprises,production scheduling plays a vital role in the production and operation management of enterprises.With the continuous development of science and technology and the continuous improvement of people's living standards,the demand for individualization has become increasingly prominent and the multi-variety,small-batch,and customized production mode is being continually developed,which lead to the traditional scheduling methods having not been able to solve the modern and complex production scheduling problem wel.The intelligent algorithms emerge as the times require.Genetic algorithms and particle swarm optimization algorithms are intelligent algorithms that have attracted much attention in recent years.The genetic algorithm searches for the optimal solution by simulating the natural evolution process.The global search ability of the genetic algorithm is strong but the convergence speed is slow owning to lacking the guidance information in the process of calculation.Compared with genetic algorithm,the particle swarm algorithm,designed by imitating the group activities of the biological world,is easier to implement because of fewer parameters and has a fast convergence.However,the particle swarm optimization algorithm will lead to the deterioration of population diversity and fall into local optimum in the later stage.The hybrid algorithm was constructed by mixing the two algorithms above by some scholars to improve the performance of single algorithm of solving complex scheduling problems and proved to be valid.The neighborhood search algorithm was used to improve the hybrid algorithm of particle swarm optimization and genetic algorithm and the classical cases were tested to verify the effectiveness of the improvement on the optimization performance.The domestic and international research trends of problems related production scheduling was reviewed and the basic principles and improvement methods of two algorithms were studied in this thesis.Besides,the feasibility of the two algorithms were demonstrated by comparing the advantages and disadvantages of the two algorithms and analyzing the advantages and significance of solving the shop scheduling problem with the hybrid algorithm.The neighborhood search algorithm was used to improve the hybrid algorithm by introducing four kinds of search operators,including the insert operator,the inverse operator,the swap operator and the pairwise operator,to expand the local search domain and improve the local search ability.Finally,the improved hybrid algorithm was used to calculate the classic cases FT06,LA31 and Car5 adopted by many scholars.Compared with the calculation results of the genetic algorithm,particle swarm optimization and other improved algorithms,the improved hybrid algorithm proposed in the thesis was proved to be more superior in terms of the convergence speed,the computational accuracy and the global search capability.