The Optimization of Multi-stage Production Process Based on the Combination of MDP and PSO

Abstract

Amidst the increasingly intense competition in the electronic product manufacturing industry, companies face the dual challenge of controlling defect rates and managing costs in multi-stage production processes. To address these issues, this study proposes a novel model that combines the Markov Decision Process (MDP) with Particle Swarm Optimization (PSO) to optimize multi-stage production decisions. MDP establishes a decision-making framework through state transitions and reward functions, enabling each step of the production process to adjust dynamically based on real-time conditions. Meanwhile, PSO performs global optimization to identify the optimal strategy combinations. Experimental results indicate that the new model demonstrates significant efficiency advantages in handling uncertainties and multi-stage decision-making, improving solution speed by over 30% compared to traditional methods. Additionally, it shows remarkable effectiveness in defect rate control and cost optimization, contributing to a 15-25% increase in corporate revenue. By innovatively integrating MDP's decision-making framework with PSO's global optimization capability, this study addresses the limitations of traditional methods in complex production environments, offering a highly practical decision-making solution for quality and cost management in electronic product manufacturing.