Research On Application Of Swarm Intelligence Optimization Algorithm To Job Shop Scheduling Problem

Flow shop scheduling production mode is a common production mode in modern manufacturing industry,a production sequencing model common in actual production process,and an important issue in the field of workshop scheduling research.The optimization objective of the scheduling problem is to arrange a reasonable processing sequence for the jobs to be processed based on constraints and production needs to meet customer needs and enterprise production requirements.Production scheduling is the foundation of an enterprise’s production activities.A reasonable scheduling plan can realize a smooth and orderly production process,minimize waste and improve production efficiency,which is of great significance to improving the satisfaction of enterprises and customers.In the sense of theoretical research,the shop scheduling problem is a typical NP-hard problem.The solution to such problems involves multidisciplinary theoretical knowledge such as operations research,mathematics,computing science,and industrial engineering.In-depth study of scheduling problems can continue to promote scheduling theory.The integration with other disciplines also has important guiding significance for other combinatorial optimization problems.Genetic algorithm is one of the most commonly used algorithms for solving scheduling problems.Because the overall search strategy and optimization process of the calculation process does not depend on gradient information or other auxiliary information,it is only affected by the objective function or the fitness function of the solution problem.widely.However,the algorithm has low computational efficiency when solving large-scale problems,and it is easy to fall into a local optimal problem,which is also a problem faced by many algorithms.In order to overcome the shortcomings of the algorithm,based on the genetic algorithm framework,a new hybrid improved genetic algorithm(NHIGA)was proposed to solve the No-wait Flow Shop Scheduling Problem(NWFSP).In order to obtain a better initial population,the NEH(Nawaz-Enscore-Ham)heuristic algorithm is improved to construct the initial population,to avoid the possibility of the algorithm falling into a local optimal solution.Secondly,the dominant block is constructed through association rule data mining,and the dominant individuals are selected to form the dominant population.The dominant genes in the dominant population are selected to form the dominant block,which helps the algorithm reduce the search space dimension and the complexity of the problem,and improves the algorithm search efficiency.In the genetic process,a co-evolutionary algorithm is used to improve the genetic evolution process,and different crossover and mutation probabilities are given to the population through segmentation,which effectively improves the evolutionary efficiency and global search ability of the algorithm,avoids invalid crossovers and mutations,and increases the amount of computation.Through the guidance of business individuals,the process enables the population to evolve in a better direction.In order to further search for the optimal solution and ensure the quality of the solution,a neighborhood search structure based on the NEH heuristic idea is proposed.The chromosome is destroyed first,and then it is reorganized according to the NEH idea to optimize the neighborhood structure of the population and improve the algorithm’s solving ability,improve the adaptability of the solution,and speed up the global convergence of the algorithm.Finally,the generated population is selected by a binary competition method.In order to verify the feasibility and effectiveness of the algorithm,the research uses the series of standard test cases of Carlier,Reeves and Taillard in OR-Library to simulate and verify the algorithm proposed in this paper.The solution results are compared with the existing research results.This illustrates the feasibility and effectiveness of the proposed algorithm in this study.The convergence speed of the algorithm proves that the injection of the superior block improves the convergence performance of the algorithm,and its solution quality also proves that the designed genetic evolution operation improves the accuracy of the algorithm and reduces the possibility of the algorithm falling into a local optimal solution.