Optimization of production decisions based on modeling and simulation

Optimizing production decisions to balance cost efficiency and product quality is a persistent challenge in modern manufacturing systems. This paper tackles the multi-stage decision-making problem in electronic manufacturing, with a focus on the inspection, disassembly, and replacement of defective spare parts and finished products. We introduce a cost-driven decision framework grounded in mathematical modeling, which integrates inspection strategies, disassembly processes, and replacement logistics to maximize net profit. The model is optimized using 0-1 programming and examines two scenarios: one involving two spare parts and another with eight spare parts and three semi-finished products. To streamline the decision-making process, we combine analytical methods with specific production constraints. Additionally, we employ Monte Carlo simulations and simulated annealing to determine the optimal profits for finished products in each scenario, thereby validating the effectiveness of our proposed solution and showcasing its applicability across various industrial contexts. Our contribution to the literature lies in providing a scalable and practical approach to multi-stage production decision-making under uncertainty, based on mathematical modeling, with potential applications spanning diverse manufacturing industries.