Planning Production and Equipment Qualification under High Process Flexibility

• 成果类型: Article
• 署名作者: Li, Hongmin; Huh, Woonghee T.; Sampaio, Matheus C.; Keng, Naiping
• 署名单位: Arizona State University; Arizona State University-Tempe; University of British Columbia; Northwestern University
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1111/poms.13439
• 发表日期: 2021
• 页码: 3369-3390
• 关键词: unrelated parallel machines; dependent setup-times; weighted completion-time; scheduling problem; sequence; algorithm; eligibility; tardiness; performance; heuristics

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.