Author(s)

Yutong Zhang ¹

Affiliation(s)

¹ School of Electronics and Information Technology (School of Microelectronics), Sun Yat-Sen University, Guangzhou, 510006, China

Corresponding Author

Yutong Zhang

ABSTRACT

Product quality is crucial for a company's competitive edge. In the assembly process, a single defective part can render the entire product substandard; even with all parts in order, the finished product may still be defective. For non-conforming products, companies can choose to scrap or disassemble them, incurring costs but recovering parts. Additionally, companies bear the costs of replacing products returned due to quality issues. This article analyzes and models multi-stage decision-making in product manufacturing, aiming to provide effective solutions. By integrating optimization modeling with statistical and managerial theories, including dynamic programming, integer programming, branch and bound methods, and genetic algorithms, it addresses various scenarios faced by companies during production: from part procurement and testing to assembly, and from product sales to the handling of substandard products. An efficient, scientific, and economical testing and production decision-making plan is designed to maximize profits or minimize costs by optimizing these decision points. This not only helps companies improve product quality, reduce production and rework costs, but also enhances their market competitiveness.

KEYWORDS

Statistical Testing, Integer Programming, Genetic Algorithms

CITE THIS PAPER

Yutong Zhang, Research on Multi-stage Decision Optimization Models in the Production Process. Industrial Engineering and Innovation Management (2024) Vol. 7: 69-77. DOI: http://dx.doi.org/10.23977/ieim.2024.070408.

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