企業為了要達成訂定的績效目標，將生產製造的流程進行精實管理，從整體的規劃生產、勞動力與庫存基準等各項變數，鎖定並解決各式變動問題，以滿足營運計劃所訂定的績效目標，這樣的管理技術稱為整體生產規劃 (Aggregate Production Planning; APP) 。APP的執行過程之中，規劃者需要同時考慮多個目標，並經由調整產能、人力資源方面的招聘與裁員、是否需要加班、庫存基準的變動調整、延期交貨，以及其他的可控變數，加以整合為滿足需求預測的多目標決策問題 (Multi-objective Decision Making; MODM)。目前常應用於APP有好幾種方法，為了追求更接近績效目標，本研究提出運用理想解相似度順序偏好法 (Technique for Order Preference by Similarity to Ideal Solution; TOPSIS) ，將經由控制距離的測量，把多目標轉換為雙目標，一個是距離最短的正理想解決方案 (Positive Ideal Solution; PIS)，另一個是距離最長的負理想解決方案 (Negative Ideal Solution; NIS)，接續應用max-min法則運算來平衡滿意度，化解PIS與NIS雙目標之間的衝突，以折衷方式 (Compromise Programming; CP) 的妥協規劃，獲取最接近績效目標的理想解決方案 (Ideal Solution)，並運用一個實例，加入其它三種規劃方法與本本研究提出的方法，本研究模型獲得最佳利潤，相較其它三種解法為最接近最優解的有效方法，於實際應用決策問題的優化效果，做為解決APP規劃問題的一個創新方法。

To achieve performance goals, companies often streamline their production processes based on variable labor, inventory, and other factors that meet the requirements of their business plans. This management technique is called Aggregate Production Planning (APP) and involves multi-objective decision making to optimize production. Planners must consider multiple objectives simultaneously and can do so by adjusting production capacity, staffing levels, overtime, and inventory levels to meet demand forecasts. There are several APP methods, but this study proposes an optimization method that converts multiple objectives into two by using the Technique of Order Preference by Similarity to Ideal Solution (TOPSIS) to measure control distances. One is the Positive Ideal Solution (PIS), which has the shortest distance, and the other is the Negative Ideal Solution (NIS), which has the longest distance. To balance the satisfaction and resolve conflicts between these two, the Max-Min rule is applied and the ideal solution that is closest to the performance goal is determined using Compromise Programming (CP). We prove the effectiveness of this optimization model using an example, we can construct the research model by incorporating the other three planning methods along with the proposed method in this study. This research model obtains the best profit. Compared to the other three methods, this approach proves to be the most effective in reaching an optimal solution. By utilizing the optimization effect of real-world decision-making problems, we offer an innovative approach to solve the APP planning problem.

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