Research on Decision-Making Optimization of Spare Parts Production Based on 0-1 Programming Model

This study establishes a binary integer programming model to optimize decision-making in the production, testing, assembly, and sales processes of spare parts. By defining decision variables and constructing a profit function, the model effectively identifies optimal production strategies to maximize profits. By adopting both genetic algorithms and simulated annealing algorithms to solve the model and conduct comparative evaluations, the superiority of the former was highlighted. To ensure comprehensive analysis, genetic algorithms and simulated annealing algorithms are employed to solve the model and evaluate its performance. This dual approach ensures that the model's effectiveness is assessed from multiple perspectives, providing both comparative insights and improved reliability in practical applications. However, the model has certain limitations, including inaccuracies in sample size calculations, limited scalability for multi-process systems, and challenges in addressing uncertainties related to defect rates. This research provides a robust decision-support tool for manufacturing enterprises, holding significant theoretical and practical value.