排程是製造流程中不可或缺的一個環節，目的是在有限的資源中，藉由工作順序及機台分配，降低生產成本並提高企業的經濟效益和競爭力。現今的工廠內部大多是屬於多個加工階段、多個機台的複合式生產環境，而本研究所探討的混合流線式生產 (Hybrid Flow Shop, HFS)就是現今許多製造業的生產環境。

本研究同時考量了學習效應(learning effect)，工廠內的人力會經由重複性的加工動作累積經驗而減少作業時間、增加效率，為了避免學習效應的過度擴張，加入學習中斷點，讓學習效應更合理。在傳統排程中往往忽略學習效應的影響而導致結果偏差，本研究將學習效應導入大規模的生產排程問題中，能提供更符合現實狀況的排程決策。
本研究藉由建立一個混整數非線性規化模型來描述具中斷點學習效應的生產線情況，並使用能求解非線性問題的優化軟體Lingo進行求解，然而考慮到求解效率，在面對大規模問題時將以瓶頸效應和限制理論的概念建構演算法RR-MOD求出近似解，並於前測結果中確定了RR-MOD的求解穩定性，再將RR-MOD和常見的啟發式演算法進行比較，最後透過實際跨國工廠的內部資料來證實本研究提出的RR-MOD啟發式演算法。

實驗結果證明了本研究所提出的RR-MOD相較於其他求解排程問題的啟發式演算法具有更高的求解力和效率，而且也能夠在短時間解決實際工廠的大規模排程問題，平均可以改善13.6個加工工作天，也顯示了本研究提出的啟發式演算法不僅能有效求解問題，未來更有發展至其他排程環境的潛力。

Scheduling is the most crucial part of production management. Enterprises can reduce cost and enhance competitiveness by scheduling the important resources. Nowadays, most manufacturing environments become more complex. Hybrid Flow Shop system (HFS) is a combination of flow shop and parallel machines, it is a common system for modern factories.

As the manufacturing scale grows, more and more people will be involved. This will make the learning effect become a key factor of scheduling. However, there is a limit to human’s learning. That is, the concept of “truncated” is needed to be considered because learning effect will definitely stop at some point.

In this research, we proposed two methods to solve the above problem: mathematical programming and heuristic algorithm methods. At first, we construct a mixed integer non-linear programming model which can find optimal solution. In order to enhance the efficiency, we proposed a heuristic algorithm: RRMOD which is composed by machine selecting rule and scheduling decision rule.

To assure the efficiency of RRMOD, we compare RRMOD with other algorithms which are commonly used to solve similar scheduling problems. In addition, to demonstrate the ability of RRMOD to solve the real HFS scheduling problem. A case study from a clothing factory is used to as an example and feasibility of the RRMOD algorithm.

From the results, we found that mathematical programming can provide an optimal solution. However, it performs inefficiently when problem’s scale become large. On the contrary, the RRMOD algorithm performs well regardless the scale of problem. In addition, it effectively improves the original schedule of the clothing factory.

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