Flexible Job Shop Scheduling Problem And Algorithm Optimization Of Manufacturing System

With the rapid development of advanced manufacturing technologies,advanced management models,informatization and market demand has gradually shifted from singleness to diversification.Personalized and customized products have become the development trend of manufacturing enterprises.Manufacturing companies are faced with the manufacturing environment of resource diversification,information sharing,global competition,and new technological innovation.This is both a challenge and an opportunity.How to better configure the flexible resources in the manufacturing system is the core problem of the development of manufacturing enterprises.However,with the increase of the complexity of the manufacturing system and the increase of the number of flexible resources,the complexity and difficulty of optimal scheduling also increase dramatically.Therefore,the in-depth study of this problem has practical value and theoretical significance.Studying the flexible job-shop scheduling problem of manufacturing system under indeterministic conditions is the focus of this thesis.With the single objective optimization model of flexible job shop scheduling as the starting point,an improved genetic algorithm is used to optimize the maximum workpiece completion time.As a powerful and often used evolutionary strategy,genetic algorithm has strong global search ability.However,the local search ability of the algorithm is weak,which leads to the possibility that the feasible solution will fall into a local optimum before the global search.In order to overcome this shortcoming,a tabu search algorithm is used to guide the local search process.The hybrid algorithm can fully diversify the search space.In the specific process,effective genetic operators were proposed based on the chromosome structure using segmented integrated coding and interpolation based greedy algorithm decoding.Through the calculation of the benchmark problem,the proposed algorithm has achieved significant improvement in the quality and efficiency of the solution.According to the hot issues in the actual manufacturing system,in addition to the load of the machine,the tardiness,and the completion time,the dynamic and static rates are introduced,and a multi-objective flexible job-shop scheduling is re-established.The model,which fully considers the production cost and operating cost is better for the flexible job-shop scheduling.The NSGA-II algorithm is used to solve the defects of local convergence of FJSP.The immune and entropy principles are used to improve the selection strategy.The distribution function is introduced to improve the elitism retention strategy to better maintain population diversity and successfully avoid premature convergence and local convergence.Through the test calculation and example simulation of the benchmark problem,themulti-objective flexible job-shop schduling model constructed in this thesis and its improved algorithm are feasible and effective.Finally,combining the low-carbon and energy-saving issues faced by manufacturing companies,with the goal of minimizing the total delay time and minimizing the total carbon footprint,a flexible job shop scheduling model with low-carbon emission constraints was established.The model is solved by using the NSGA-II algorithm proposed in this thesis,and a satisfactory Pareto optimal solution set is obtained.In view of the novelty of the carbon footprint optimization method,MOGA and SPEA2 algorithms were used as comparison algorithms.The comparison of examples and multiple evaluation criteria verified the feasibility and effectiveness of the carbon footprint shop floor scheduling optimization method and NSGA-II algorithm.