本文延伸最小總成本的觀念，推導出具有良好遞迴關係的最低總成本法（Minimal Total Cost, MTC），以決定動態經濟批量的最佳解而非近似解。文中發展最低總成本的觀念並提出數學模式以求得最佳之訂購批量，並且推導出一些定理來簡化最低總成本法的求解過程，使其具有易於使用的特性；另一方面，本文的MTC法可適用在各期單位儲存成本相等或不相等的情況，以及各期訂購成本相等或不相等的情況，使得其具有較廣泛的適用性。進而，本文利用電腦模擬的方式，就電腦計算最佳批量所須的CPU時間，來比較最低總成本（MTC）法與Wangner-Whitin（W-W）方法之演算效率，結果顯示：MTC法的CPU時間明顯的比W-W法要少，而且計劃期數越長，此種趨勢越明顯。最後，根據模擬所得的數據，進一步求得二種方法的迴歸方程式，並據以作更長的計劃期之CPU時間預測，所得到的結果亦如上述。

　　　　　This paper extends the concept of Least-Total-Cost(LTC), offering the Minimal Total Cost(MTC) approach, which has an excellent recursive relationship. This approach is used to determine optimal solutions to dynamic economic lot size instead of approximations. This paper develops the concept of minimal total cost and proposes mathematical models for finding optimal solutions to order quantities. And several theorems that simplify the solution procedure of the MTC approach are introduced, marking the MTC approach easy to use. In another respect, the MTC approach can be used in situations where unit carrying costs per peiod are equal or unequal, and ordering costs per period are equal or unequal, giving the MTC approach a widely application. Further, this paper uses computer simulation, based on the CPU time required by a computer to calculate optimal lot sizes, to compare the computational efficiency of the MTC approach with that of the Wagner-Whitin (W-W) approach. The results show that the MTC approach clearly uses less CPU time than the W-W approach. Moreover, as the planning horizon increases, this trend becomes even more pronounced. Finally, the data from the simulation is used to find regression equations for the two approaches. These equations are then used to predict CPU times required by the two approaches for even longer planning horizons. The results are identical to those described above.