考虑不确定缺陷率和废品率的闭环制造

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考虑不确定缺陷率和废品率的闭环制造-再制造系统网络设计基于情景的机会约束程序设计

摘要

为了通过延长生产材料的生命周期和剩余价值来促进可持续发展，研究了具有不确定性干扰的闭环制造系统的协同调度方案。根据闭环制造系统在多产品、多周期情况下的特点，建立了一个混合整数线性优化模型，通过回收在制品和最终产品中的生产材料来协调制造和再制造单元。在此基础上，提出了一个新颖的闭环制造-再制造调度框架。此外，针对产品质量和加工能力的变化，还引入了基于情景的机会受限编程，以解决潜在的不确定性来源。随后，设计了一种顺序优化方法，以降低计算复杂性。最后，研究了一个来自电子厂的工业案例，通过与其他几种策略的比较，实施所提出的方法来证明其适用性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Design of closed-loop manufacturing-remanufacturing system network considering uncertain defect and waste rate: Scenario-based chance-constrained programming

To facilitate sustainability by extending the life-cycle and residual value of production materials, a collaborate scheduling scheme for closed-loop manufacturing system with disturbance uncertainty is studied. Based on the features of the closed-loop manufacturing system under multi-product and multi-period scenarios, a mixed-integer linear optimization model is developed, where manufacturing and remanufacturing units are coordinated by recycling the production materials from work-in-process and final products. Based on this, a novel closed-loop manufacturing-remanufacturing scheduling framework is proposed. Additionally, in response to variations in product quality and processing capabilities, a scenario-based chance-constrained programming is introduced to address potential sources of uncertainty. Subsequently, a sequential optimization method is devised to reduce computational complexity. Finally, an industrial case from the electronic factory is investigated to demonstrate its applicability by implementing the proposed approach with comparison to several other strategies.

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来源期刊

期刊介绍： Expert Systems With Applications is an international journal dedicated to the exchange of information on expert and intelligent systems used globally in industry, government, and universities. The journal emphasizes original papers covering the design, development, testing, implementation, and management of these systems, offering practical guidelines. It spans various sectors such as finance, engineering, marketing, law, project management, information management, medicine, and more. The journal also welcomes papers on multi-agent systems, knowledge management, neural networks, knowledge discovery, data mining, and other related areas, excluding applications to military/defense systems.

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