Research On Flexible Flow Shop Scheduling For Typical Automobile Parts Production

In order to adapt to the complex market environment and the improvement of manufacturing system flexibility under the MTO manufacturing mode,to help enterprises better reduce cost and increase efficiency,thereby enhancing competitiveness,this paper studies the flexible flow shop scheduling problem.Oil cooler products of M company as the object,this paper analyzes the typical automobile parts production characteristics,and building of ordering production oriented and considering the flexible flow shop scheduling problem model of parallel process.In this paper,a single-objective scheduling method based on improved genetic algorithm and a multi-objective scheduling method based on improved NSGA-Ⅱ algorithm are proposed to solve the problems under the model and provide guidance for the actual production of enterprises.The specific work is as follows:(1)A flexible flow shop scheduling model for typical automobile parts production characteristics is constructed.Taking the oil cooler production workshop of M Company as a typical case,this paper analyzes the characteristics of order-type production mode,points out that the deviation between delivery time and completion time will result in the penalty cost of order time,and the order quality level will affect customer satisfaction,and points out that there are parallel processes in production.A flexible flow-shop scheduling model with economy,environmental protection and customer satisfaction as decision objectives is constructed.The model considers the influence of parallel operations on objective functions and constraints;(2)In view of the existence of single-objective and multi-objective scheduling problems in actual production,the improved genetic algorithm and the improved NSGA-Ⅱ algorithm are designed to solve the problem under the model.Aiming at the limitation that the search ability of traditional genetic algorithm and NSGA-Ⅱalgorithm is affected by parameter setting,the crossover and mutation probability with the number of iterations is designed.In order to improve the practicability of the final solution in multi-objective scheduling,the initialization strategy based on decision preference is designed in NSGA-Ⅱ algorithm.The convergence speed of the improved genetic algorithm is improved by 66.7%,the optimal value,average value and variance of the final result are better,and the solution set coverage and optimal value of the improved NSGA-Ⅱ algorithm are better.In conclusion,the improved algorithm has certain effectiveness;(3)Based on the actual data of M company oil cooler production workshop,a case study was carried out.Based on the actual situation of the production shop and the historical order data,a detailed oil cooler shop scheduling model was constructed,and the improved genetic algorithm and the improved NSGA-Ⅱ algorithm were used to solve the scheduling problem under the model respectively.The results of this study were compared with the historical order results.The economic index of single objective scheduling problem decreased by 18.6%,the environmental index decreased by 24.84%,and the customer satisfaction level was high.The economic index of multi-objective scheduling problem decreased by 15.8%,the environmental index decreased by 22.32%,and the customer satisfaction level was high.In conclusion,this study has certain feasibility and validity.