不完全信息场景下基于成本最小化的生产决策研究  

Research on production decision-making based on cost minimization under incomplete information scenario

作　　者：朱再胜 洪昙飞 施位徽 姚文哲 顾天乐 刘会平 ZHU Zaisheng;HONG Tanfei;SHI Weihui;YAO Wenzhe;GU Tianle;LIU Huiping(School of Digital Technology and Engineering,Ningbo University of Finance&Economics,Ningbo 315175,China;Lingzhi Equipment Co.Ltd.,Yixing 214215,China)

机构地区：[1]宁波财经学院数字技术与工程学院,浙江宁波315175 [2]凌志装备股份有限公司,江苏宜兴214215

出　　处：《延边大学学报(自然科学版)》2025年第1期13-18,共6页Journal of Yanbian University(Natural Science Edition)

基　　金：东营市科学基金项目(DJB2022017).

摘　　要：在零配件、半成品和成品的次品率不完全确定的情况下,以成本最小化和质量控制为目标建立了最小化成本效益模型.该模型利用抽样检测方案来确定抽检样本量,运用遗传算法对模型进行求解来得出最优抽检策略.实例验证结果表明,该方法既能考虑产品质量,又能锁定最低成本的决策路径,因此能够为企业生产决策提供良好的技术参考。In the case of a defective rate of spare parts,semi-finished products and finished products is not completely determined,a minimized cost-effectiveness model is established with the goal of cost minimization and quality control.The model uses a sampling detection scheme to determine the sample size.The genetic algorithm was used to solve the model,and the optimal sampling strategy was obtained.The validation results demonstrate the method's capacity to evaluate quality and identify the most cost-effective decision path.These findings serve as a robust technical reference for enterprise production decision-making.

关 键 词：不完全信息 成本最小化 遗传算法 生产决策 

分 类 号：TP21[自动化与计算机技术—检测技术与自动化装置]