本研究探討具有等候時間限制製程下工件允入決策控制法則，讓管理者能在工件來到等候時間限制區特定製程段前端時作最佳決策，降低所有工件重工機率，進而產品提高良率。等候時間限制問題，是指在限制的時間內在製品必須完成特殊製程的加工，若當等候時間超出時間限制時，在製品必須重新加工或報廢，嚴重影響工件品質和生產成本，甚至引起客戶抱怨而失去訂單。實務上，高階40、28奈米製程晶片電路更精細，生產過程中對於金屬線的氧化時間限制更嚴格， 若控制不當則會造成金屬線的氧化而造成晶片良率不佳而報廢。高階製程中，生產管理者會設定更嚴格的等候時間限制來管理產品良率。實務上，約有60%的製程中設定等候時間限制，超過70%超過等候時間限制必須報廢，因此等候時間限制問題在半導體實務上是非常重要的。其亦廣泛應用在TFT-LCD面板生產鏈、鋼鐵業、食品加工業中以確保產品的品質。
在多機台群組下機台產能與產品品質衝突下，面臨著需求不確定下與機台間的不確定性當機下的生產管理。決策目標皆為最小化在製品等候成本及報廢成本，作為等候時間限制區特定製程時之最佳允入決策。最後比較傳統先進先出法，固定式閘門控制(threshold control) ，固定時間重新運算(recation chain) ，並經由模擬實驗結果發現最佳化隨機動態規劃模式在產品重工數、最小成本及總產出量三種績效指標皆表現優異。最後，本論文對等候時間限制製程下工件允入決策控制法則，期望能幫助實際的生產線課長作最佳決策，以作為後續實務上應用的基礎。

This research examines production control problems in two-station serial production systems under process queue time (PQT) constraints. In these serial production systems, all jobs must be processed at a fixed order in the upstream and then downstream stations. There are multiple machines in both stations, and all machines are subject to random machine failures. In the downstream queue, the sum of waiting and processing time for each job is limited by an upper bound. This upper bound of time is called the PQT constraint. Violation of the PQT constraint causes high rework or scrap costs.
An important application of this research is the control of semiconductor fabrication processes. In advanced (20, 28, and 40 nanometer technology node) 300mm semiconductor manufacturing systems, 60% of the manufacturing steps are PQT related. Any violation of PQT constraints seriously impacts yield quality and incurs significant scrap costs. In addition to semiconductor manufacturing, PQT constraints are common in other industries, including Thin Film Transistor Liquid Crystal Display (TFT-LCD), food processing production and steel production.
In this research, an admission control model is formulated by Markov decision processes (MDP). In practice, job arrivals are random and unknown in advance. Moreover, servers and machines are not reliable and subject to failures and maintenance. Because real time reliability status of all machines is explicitly considered, computational efficiency suffers from the well-know “curse of dimensionality” of dynamic programming. To overcome the computational complexity issue, we prove the existence of optimal exhaustive production control policy. Based on the existence of optimal exhaustive control policies, an efficient algorithm is designed to significantly reduce computational time. Compare with other control methods in literature, significant performance improvement is observed in our simulation study.

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