本研究對有彈性零工式流程特性，還有迴流加工作業特性之多重產品開發專案環境排程問題進行研究，透過平行機台排程、零工式流程排程、彈性零工式排程問題、迴流式流程排程與多重專案排程相關的文獻探討，針對在同時間有專案重疊數大於平行機台數的工作站透過數學規劃方法進行資源衝突決策，建構以總加權延遲工時最小化為目標之多重產品開發專案排程模型。
在本研究中分別建構非線性(INLP)與線性(ILP)數學規劃模型，針對第一個時間點發生重疊兩個產品開發專案以上且機台資源不足的工作站，進行LINGO求解，決定各專案於該工作站的加工優先順序，解決資源衝突問題。在重新排程後，重複進行資源衝突決策，直到沒有機台資源衝突問題為止。
實例驗證比較本研究提出之INLP及ILP數學規劃模型，結果顯示，總求解時間、延遲專案數、總延遲與總加權延遲的表現，不論是以完成時間或完成日期為比較基準，ILP皆優於INLP。在實務環境中，專案關注的是以完成日期為基準，若在本研究提出之基本假設條件下，ILP數學規劃模型表現亦優於個案公司目前之優先權方法，因此採用ILP數學規劃模型可得較佳之答案。

This study focuses on the flexible job shop scheduling problem of multiple product development projects with reentrant process. We construct two mathematical models, nonlinear programming (INLP) and integer linear programming (ILP), to deal with the decision-making of resource conflicts. The resource conflicts occur when we simultaneous schedule two or more projects in the same workstation, and the number of projects is greater than the number of parallel machines. The objective function of multiple product development projects scheduling model is the minimization of the total weighted tardiness. First the activities of each project are scheduled without resource constraints and then the schedules are integrated to see whether there is any resource conflict. If there are resource conflicts, the first encounter resource conflict is resolved by one of the mathematical models. The mathematical models are solved by LINGO to schedule the timing of all activities assigned to each machine of that workstation. After solving by LINGO, the integrated schedule is remodified and then repeat the resource conflict decision-making until there are no more resource conflict. Comparing INLP with ILP mathematical model, the experiment shows that, for the total elapsed runtime, the number of delay projects, the total tardiness, and the total weighted tardiness, the performance of ILP is better than INLP. For the practical situation, the requirement of projects is the due date, and simulation results with the assumptions of this study, demonstrate that ILP mathematical model outperform current methods used in practice.

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