Programming Model for Enterprise Multi-Process Decision Optimization

Abstract

This paper addresses critical decision-making challenges faced by enterprises during the production process, particularly focusing on efficient quality inspection, inspection and disassembly decisions, defective rate control, and cost minimization. In the current dynamic market environment, existing algorithms demonstrate insufficient adaptability, hindering their effectiveness in addressing complex production scenarios. To overcome these limitations, we propose a novel genetic algorithm that incorporates dynamic change adjustment through a 0-1 programming model. This approach enhances the algorithm's flexibility, enabling enterprises to develop scientifically sound and rational production decision schemes tailored to varying market conditions. Our empirical analysis reveals that the average production cost for enterprises utilizing this method is 32.7, indicating significant potential for cost reduction while maintaining product quality. The findings contribute to the existing literature by providing a robust theoretical framework that underscores the importance of adaptive algorithms in improving production efficiency and decision-making processes in an ever-evolving market landscape.