電子產品在沒有創新的產品或技術產生，其跌價趨勢是非常明顯的。尤其在金氧半場效應電晶體(Metal-Oxide-Semiconductor Field-Effect Transistor, MOSFET)產業，因為產值不大，管、銷、研費用無法以較高的營業額來分攤，大陸新進業者也挾持著政府的奧援，打擊了台灣業者的經營。在這麼嚴峻的產業環境，全面性的生產與物流規劃顯得十分重要。
為了達成利潤極大化，考慮了生產條件、物流環境、客戶需求與銷售利潤，提出數學規劃模型。在此模型中，輸入工廠產能、客戶需求、運費資訊、庫存狀況、生產成本、單位售價等資訊，可以立即規劃出各工廠的生產量、各地點間的運送數量與次數、各地點的交貨量。藉由此求解結果，生產計畫人員、業務人員、財務及高階管理人員可以獲得其所需的資訊，還可藉由變更部分數據，立即試算其所造成的效益或損失。
本研究之數學模型為一整數線性規劃(integer linear programming, ILP)問題，在考量各公司與研究機構的求解軟體後，以Gurobi為求解軟體、MATLAB為執行介面來計算最佳化數值。以實例套入求解後，分析求解數據之合理性，並與個案公司實際作業數值進行比較，證明此數學模型是可用的，並可以增加公司整體利潤。
雖然此模型已可套用於公司實際作業中，但在計畫週期、原物料的供應、非統包的模式、資料規模等方面的規劃還是不夠完善，未來還需要繼續深入研究。

The price of electronic products trends down obviously without innovative technology. The expenses of management, marketing and research cannot be shared because of low output value, especially in the Metal-Oxide-Semiconductor Field-Effect Transistor (MOSFET) industry. New entrants from China beat Taiwan industry by holding assistance of their government. In such a harsh industrial environment, production and logistics planning with comprehensive consideration is very important.
In order to maximize profit, mathematical programming model which consider production condition, logistics, customer demand and margin was proposed. Manufacture quantity of each factories, transport times, transport quantities between nodes and shipping quantities from each node can come out from this model immediately with input of production capacity, customer demand, freight rate, inventory, production cost and selling price. Production planner, sales, financial officer and top managers can get some information from these results. Benefit and lost can be evaluated by changing some parameters in this model.
The mathematical model of this study is an integer linear programming (ILP) problem. After comparing some software from various companies and research institutions, Gurobi was chose to optimize the problem, and MATLAB was applied to be the interface. The Model is useful and can raise profit after analyzing the rationality of result and comparing the result with actual data for a real case.
Although the model can be applied to operations of real companies, this research did not give careful consideration to planning cycle, supply of raw materials, non-turnkey model and huge scale of data. More efforts should be made in the future.

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