本文分別針對考量交易成本因素及運輸成本因素，而構造二種不同的EOQ推廣模式。構建第一種EOQ推廣模式背景：傳統的存貨模式假設某一產品的需求率為固定的常數；如果產品的需求是因產品被銷售而造成，則上述假設相當於存貨的銷售率是固定的。實務上，往往因為經銷商交易成本投入的增加對於消費者交易成本支出減少的影響顯著，而使得交易成本與售價兩因素皆成為存貨決策的重要變數。因此在構建本模式前必須先討論，如何透過消費群體之交易成本與願付價值上限的二元分配建構一特殊需要函數的問題，本文的研究成果之一即是將此特殊的需求函數，以數學函數具體的表達出來，並透過敏感度分析探討此需求函數的特性及管理意涵。接著，利用此特殊需求函數的特性，建構一個可透過售價控制銷售率之EOQ模式，討論經銷商應如何決定售價、單位交易成本投入來控制銷售率及期初存貨水準，使其利潤最大之問題（包含相關參數變動對最佳存貨決策變數的影響效果），則為本模式的應用內容。
在傳統的存貨模式中，產銷過程中之運輸成本常被視為生產成本或訂貨成本的一部分。然而在實務方面，以台灣為例，基於全球佈局策略的考量及台灣與中國大陸地理位置的接近性，已有許多企業將製造中心散佈在中國大陸，而將加工、包裝及行銷中心設立在台灣。因此，廠商面臨轉運問題時，決策者必須同時考量如何訂購貨品數量，以及如何租用有容量限制以及有連續工作小時上限之車輛數，以使得單位時間之總運輸成本最小。第二種EOQ模式的延伸因而被建立來分析及討論此類EOQ模式最佳解的各種性質，此為本論文另一個研究貢獻。

This thesis constructs two distinct models from the classical EOQ model where the transaction costs and transportation costs are considered respectively. The first extension of EOQ model mainly improves the traditional inventory model in which the demand rate of a product is presumed as a constant. As usual, if the sale leads to the demand of product, the selling rate of inventory will be definitely constant. In practice, however, the expenditure of the consumer on the transaction will be significantly decreased due to the increasing investment in the transaction process for the wholesaler. Thus, the two key factors, “transaction costs and selling price,” will become the important variables that would be incorporated into the inventory decision-making process. Based on this, as constructing a realistic EOQ model, it is necessary to discuss in advance how to utilize a bivariate distribution function with the consumer willing-to-pay price and transaction costs to construct a special demand function, . The one of main contributions in the thesis is to establish such a special demand function by means of a concrete mathematical expression so that the characteristics and the managerial implications of this demand function can be revealed through the sensitivity analysis. Furthermore, this proposed mathematical model applies the characteristics of the special demand function to construct an EOQ model with controllable selling rate. In such a model, the wholesaler can control the selling rate and further determine the initial inventory level for each business cycle by means of manipulating the selling price and the unit transaction costs so that the profit of unit time is maximized.
In the traditional EOQ model, the transportation costs during the production and marketing courses are usually regarded as a part of manufacturing or ordering costs. In practice, to give an example of Taiwan, based on the consideration of global arrangement strategy and the geographical proximity between Taiwan and mainland China, many firms have dispersed their manufacturing plants in China and to establish their processing, packing or marketing centers in Taiwan. Thus, facing the transshipment problem, the decision makers must simultaneously consider how many quantities of goods should be ordered and how many container cars with limited capacity and the ceiling on continuous working hours should be rented, so that the total transportation cost per unit time is minimized. The second extension of EOQ model is therefore constructed to analyze and discuss the various characteristics of optimal solutions. That is also another contribution of this thesis.

目    錄 

目    錄	1
圖  目  錄	III
表  目  錄	V
使用符號一覽表	VI
第一章 緒論	1
1.1 研究動機與目的	1
1.2 文獻探討	4
1.3 本文結構	9
第二章 由消費者願付價值與交易成本二元分配所構建之需求函數	11
2.1符號說明與假設	11
2.2 產品供給機構之產品銷售率( )及消費者剩餘( )	14
2.3 當 為二元常態分配時之需求函數 的性質	20
2.4敏感度分析	26
2.5 本章內容大要	30
第三章 可控制銷售率之EOQ模式	31
3.1假設	31
3.2 數學模式	32
3.3 敏感度分析 (比較靜態)	37
3.4 本章內容大要	47
第四章 納入運輸成本的EOQ模式	49
4.1 符號說明與假設	49
4.2 數學模式	51
4.3 本章內容大要	65
第五章 結論	67
5.1 主要的研究結果	67
5.2 未來的研究方向	71
參考文獻	73
 

圖  目  錄

圖1.1 本文論述之問題類別及其表述結構	10
圖2.1 貨品銷售率和零消費者剩餘線 之關係。	14
圖2.2 情況1之圖示	23
圖2.3 情況2之圖示	24
圖2.4 需求函數 	25
圖  需求函數 	26
圖2.5 當 增加至 時，需求函數的變動情形	27
圖2.6 當 增加至 時，需求函數的變動情形	28
圖2.7 當 增加至 時，需求函數的變動情形	29
圖2.8 當 增加至 時，需求函數的變動情形	29
圖3.1 最佳解 的決定	37
圖3.2 參數 、 及 增加的影響效果	38
圖3.3 參數 變動的影響效果	39
圖3.4 邊際交易成本 增加的影響效果	46
圖4.1  的圖形	54
圖4.2 函數 之圖形	60
圖4.3  參考圖	64


表  目  錄

表3.1 參數變動的影響效果	48

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