Research On Multi-objective Flexible Job Shop Scheduling Problem Based On Genetic Algorithm

In the present,the issue of energy consumption is receiving global attention.The manufacturing industry also fac es with the dual tasks of reducing energy consumption and improving production efficiency.Flexible job shop scheduling is a key module in the production process of enterprises.Therefore,appropriate scheduling solutions can not only improve the production effici ency greatly,but also reduce the production costs of enterprises effectively.In this thesis,the problem of flexible job shop scheduling i s used as the research object,and multiple constraints are taken into account to build a model of the multi-target optimization scheduling problem with the goals of minimizing maximum completion time,minimizing processing costs and minimizing carbon emis sions.Finally,the improved genetic algorithm is applied to the multi-objective optimization problem.The main results are listed as follows:(1)Based on the specific characteristics of the flexible job shop scheduling problem,improvements have been mad e to existing genetic algorithms.In details,the new algorithm improves the initialization method first.Fast selection and random selection are combined to increase the quality of the initial solution in the population,thus accelerates the convergence s peed of the algorithm.In terms of selecting the operator,in order to increase the selection probability of excellent individuals to the next generation,the tournament sequencing and elite retention strategies are applied to reduce the selection pressure of the population.In terms of crossover operator and mutation operator,the commonly used machine-based interchange crossover method and the process-based insertion mutation method are improved to avoid the emergence of illegal solution,thus saving the algorithm's running time to a certain extent for repairing the illegal solution during the calculation pro cess.Finally,the validity of the proposed crossover and mutation methods in the improved algorithm were verified,and tested on some benchmark examp les.(2)The improved genetic algorithm is also applied to the multi-objective flexible job shop scheduling optimization.Based on the constrained model theory,an example of a multi-objective flexible job shop scheduling problem is simulated to verify the performance of the algorithm and the feasibili ty of the method with the goals of minimizing maximum completion time,minimizing processing costs and minimizing carbon emissions.According to the preference settings of production managers or market demand,the scheduling model is optimized into a peak season production sub-model and a slack season production sub-model.Although the three goals are not taken into account at the same time,after considering the main considerations,the remaining optimization goals are considered as constraints.The simula tion results indicate that the purpose of multi-objective optimization is achieved to a certain extent,which provides a certain theoretical reference for the production scheduling decisions for the enterprises.