A Decision Optimization Framework for Multi-Stage Manufacturing Processes: Integrating 0-1 Integer Programming and Genetic Algorithms

Abstract

Optimizing multi-stage manufacturing is crucial for improving profitability, operational efficiency, and competitiveness in the market. The complexity of multi-stage manufacturing systems has been extensively studied, with numerous approaches exploring various optimization techniques. This study proposes a decision optimization framework that addresses critical challenges in inspection, disassembly, and resource allocation. The framework integrates a 0-1 integer programming model, which captures binary decisions and key operational parameters, with a genetic algorithm (GA) designed to deliver efficient solutions for large-scale manufacturing instances. This approach builds on established optimization methods and addresses contemporary challenges in production, including resource constraints and quality control. The 0-1 integer programming model formalizes the problem, while the GA aims to maximize expected profit by optimizing resource usage and process efficiency. Case studies demonstrate significant improvements, with profit increases of 10%-25% achieved through optimized strategies, such as selective inspection and strategic disassembly. This research provides practical guidance for manufacturers, balancing quality control, cost management, and resource optimization, and bridges the gap between theoretical modeling and real-world manufacturing applications, offering insights for smarter decision-making in the manufacturing sector.