Planning Production and Equipment Qualification under High Process Flexibility

计算机科学 灵活性(工程) 地铁列车时刻表 工厂(面向对象编程) 产品(数学) 过程(计算) 生产(经济) 生产进度表 启发式 工业工程 序列(生物学) 生产计划 数学优化 可靠性工程 运筹学 调度(生产过程) 人工智能 工程类 遗传学 几何学 数学 程序设计语言 生物 经济 宏观经济学 操作系统 统计
作者
Hongmin Li,Woonghee Tim Huh,Matheus C. Sampaio,Naiping Keng
出处
期刊:Production and Operations Management [Wiley]
卷期号:30 (10): 3369-3390 被引量:6
标识
DOI:10.1111/poms.13439
摘要

We present and solve a joint production and qualification planning problem for a manufacturing environment with high process flexibility. Various factors contribute to the complexity of the problem, one of which is product‐machine mapping: Each machine may be qualified to produce multiple products and each product can be produced on multiple machines. To meet a build‐plan, the factory needs to not only determine a multi‐machine multi‐product production schedule that accounts for sequence‐dependent setup time, but also a qualification schedule which prescribes whether and when a machine should undergo a qualification process such that it is ready to produce a product. We consider processing characteristics including sequence‐dependent setups, job splitting, and machine eligibility in addition to qualification. We formulate the mathematical model as an MILP problem that minimizes the total weighted delay. In this study, we describe two heuristic solution approaches developed for this complex decision setting and the application at Intel. We compare our approach with Intel's current approach which is a spreadsheet‐based manual approach that relies on the experience of the factory planner. The results indicate that our approach, which we call the GS approach, dominates in terms of minimizing the delay. Our approach performs well when capacity is tight and additional qualifications are considered while the Intel approach may perform better on reducing the setup and qualification time in certain problem instances, particularly those with loose capacity. As a result, an integrated approach which selects the better solution from both approaches is proposed, which shows significant reduction over the current approach in both the weighted delay and the setup and qualification time.
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