近年來由於新科技的運用、國際化的趨勢及消費者意識的覺醒，使得機械產業結構逐漸改變，自大量化的生產，轉變為少量多樣與客製化、彈性化的生產，生產排程的良窳便成為反應工廠應變能力與競爭力之關鍵。對於機械製造業而言，常見的排程方式多以最小到期日之派工法則為主，當實務上遇到緊急插單時，所執行之重排程多需大幅更動原有排程結果，而造成物料不足或過剩之情形，亦影響生產排程之管控。若在緊急插單重排程時，能減少重排程的變異幅度與時程範圍，將可減少生產排程不斷改變的衝擊。
有鑑於此，本研究以國內機械製造業為研究對象，於生產排程部份，提出以滿足顧客需求及庫存成本最小化，並以產品完工日與預定交貨日相距最小化為目標之生產排程數學模式；而在緊急插單重排程部份，以不擾動原有訂單之生產排程下，提出滿足顧客需求及庫存成本最小化為目標之緊急插單重排程數學模式，並針對此兩個數學模式，以限制規劃為基礎之近代啟發式解法求解。為驗證本研究所提出之排程模式與求解方法具實務應用之可行性，本研究以國內一機械製造業者之實務資料加以分析與測試。與此公司以派工方式之排程比較，於產品如期完工、整體產能運用及生產排程擾動最小，本研究所提出之排程模式架構均具有較佳之效率，可有效解決此公司生產排程之困擾，亦可提供其他產業遭遇相類似問題時之參考用。

A good production scheduling and management is the key point of a factory
ability to deal with emergent or competitive environment. The purpose of production scheduling is to plan on going production activities to be done more efficiently.
However, planned schedule has always to be altered because of the join rush orders. When facing decision of rush orders rescheduling, if enterprise can reduce rescheduling variation range and narrow rescheduling time scope based on allowing costs, the impact to production line and enterprise will be significantly reduced. In machinery industrial, patching rule for earliest due date was scheduled by forward PERT concept. If schedule result was being changed, that caused material vary uncertainly and impacted production scheduling and management, while facing reschedule of rush orders rescheduling.
The research is focused on machinery manufacturing, developing a production
scheduling models and employing the meta-heuristic method based on constraint programming is adopted. The goal of the models is to satisfy customers demand and to minimize perturbation, minimizing inventory cost, minimizing time difference of end products to be finished and expected finished as the performance criteria. It shall provide an effective decision support system for the managers of the production planning system in machinery industry, increasing enterprise competitiveness further.
The entire experiment is performed using F Company, and provides enterprise
management production planning to verify the meta-heuristic system which helps solving problems in practice.