Research On Parallel Scheduling Problem In Die Casting Shop Considering Mold Change

The new energy vehicle industry is booming,and along with it,the demand for corresponding automotive components is growing.The production of automotive components relies heavily on die-casting companies.Company X’s die-casting workshop has a large number of equipment,complex molds,diverse products,and varying production batches.However,severe delays in product orders have resulted in significant stockout costs.Maximizing production efficiency with limited machines and molds has become the top priority for Company X.Based on this,this thesis focuses on the construction of a parallel machine scheduling problem model considering mold changeovers in Company X’s die-casting workshop.The objective is to minimize the total delay time.The thesis addresses the problem and conducts the following research:Firstly,this thesis conducted a survey and problem analysis of the current situation.It described the current production status of the die-casting workshop from three aspects: company overview,operational processes,and production scheduling.The specific mold changeover situations and machine equipment in the workshop were understood.A detailed analysis of the current situation was carried out,and specific improvement strategies were provided.Secondly,the problem was defined,constraints were determined,and a mathematical model was abstracted and an algorithm was designed to solve it.The model considered production line constraints,production sequence constraints,order release date constraints,and mold changeover constraints,with the objective of minimizing the total delay time.According to the characteristics of the problem,it was decomposed into two decision problems,order sequencing and equipment resource selection decisions and two heuristic solving rules were proposed,EDD-FAM and EDDLPT-FUMOD/FAM.In addition,a genetic algorithm was designed,and improvements were made to the initial population generation and decoding processes by incorporating the heuristic rules.Finally,case studies were conducted to validate the effectiveness of the problem model and solution algorithm.Parameter optimization simulation experiments were performed on the genetic algorithm.The case data were solved using EDD-FAM,EDDLPT-FUMOD/FAM,genetic algorithm,and improved genetic algorithm,and corresponding results were obtained.A comparative analysis was conducted on the scheduling results of each method to compare their advantages and disadvantages.Through the comparative analysis,it was found that the improved genetic algorithm was the most optimal among the other three solution methods.The thesis includes 21 figures,22 tables,and references to 80 articles.