IC製造商使用高度自動化的生產來滿足不可預測的需求模式。為了確保遵守調度計畫的日常約束，工作層管理人員需要集中精力減少操作員的總工作時間，同時確保平衡的工作量。到目前為止，大多數研究都只專注於分別尋找佈局設計或操作員分配的新演算法。
這項研究為實際應用提供了一種新的反覆運算方法，該方法要求通過同時動態地優化設施佈局和操作員分配問題來滿足日後需求的整體解決方案。
根據實際應用程式的獨特特性，設計了時間成本來衡量不同佈局設計和操作員分配下的操作員生產率。與輸送量指標相比，可以使用歷史資料或預期的調度計畫輕鬆得出時間成本，從而為現場調整和定期更新提供了極大的靈活性。時間成本度量標準考慮了操作員活動的不同組成部分（例如裝載和卸載，修復測試錯誤和行走）以及由於基於非排隊的機器干擾而導致的生產力損失。引入了工作負載平衡的懲罰項，以找到系統範圍的生產率與操作員之間工作負載相等之間的權衡。優化分為獨立的佈局和分配階段。在佈局階段，使用CRAFT演算法找到改進的解決方案，而在分配階段，使用遺傳演算法。然後，這兩個階段將反覆運算運行，以為佈局設計和操作員分配提供最終解決方案。
這項研究的方法和框架可以輕鬆地適應其他類型設施中的類似問題以及不同的調度計畫。

IC manufacturers use highly autonomous production to satisfy an unpredictable demand pattern. In order to ensure respect for the due-day constraints of the scheduling plan, work-floor managers need to focus on reducing total operator work time while ensuring a balanced workload. Most of the research so far has solely focused on finding new algorithms for layout design or operator assignment separately.
This study presents a new iterative approach to a real-world application that calls for a holistic solution to satisfies due-day demand by optimizing both the facility layout and the operator assignment problem simultaneously and dynamically.
Based on the unique characteristics of the real-world application, a time cost is devised to measure operator productivity under different layout design and operator assignment. Compared to throughput metrics, the time cost can be easily derived using both historical data or prospective scheduling plans, affording great flexibility for on-site adjustment and periodic updates. The time cost metric considers different components of operator activities (such as loading and unloading, fixing testing errors and walking) as well as productivity lost due to non-queuing-based machine interference. A penalty term of workload balance is introduced to find the trade-off between system-wide productivity and equality of workload among operators. The optimization is divided into separate layout and assignment stages. For the layout stage, CRAFT algorithm is used to find an improved solution while for the assignment stage, a genetic algorithm is adopted. The two stages are then run iteratively to arrive at a final solution for both the layout design and the operator assignment
The methodology and framework of this study can be easily adapted for similar problems in other types of facilities and for different scheduling plans.

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