經濟批?排程問題(Economic Lot Scheduling Problems, ELSP)是針對單一生產設備要生產?種以上(n ? 2)的產品時，探討產品的批?大小、生產次?及週期時間，並且調整產品週期性的生產排程，使其生產計劃為可?，又能滿足顧客長期的需求，並且使得平均總成本(包含整置成本及存貨持有成本)達到最小。有重製?況的ELSP(ELSP with Reworks, ELSPRw)是假設一產品由工廠內部製造時 (此部分稱為製造批?)，有一部份出現瑕疵，須由工廠加以修?(rework)或重製 (remanufacturing)，此部分稱為重製批量。 ELSPRw是近幾?才被探討的議題；而有關ELSP常用的幾個解法包括獨立解法、共同週期法、基本週期法和延伸基本週期法。獨立解法往往是不可行解，但可視為ELSP 的成本下限，共同週期的解答因為簡單且必是可行解，可視為ELSP的成本上限。延伸基本週期法可以取得最佳的解答，但因須蒐尋各產品的週期乘數且須做可行性排程的檢測，應用延伸基本週期法求解ELSP也最困難且最耗時。而ELSPRw的可行性排程之檢測和傳統的ELSP大不相同，以往亦無相關研究探討此一議題。求解ELSPRw有三個關鍵變數：週期乘數、週期時間和排程順序。本研究嘗試以遺傳演算法在延伸基本週期法下搜尋ELSPRw的最佳週期乘數，以二分搜尋法找到最佳的週期時間，以一簡單排程方法建立可行性排程，最後針對ELSPRw提出一新的可行性排程檢測方法。藉由上述的求解程序，本研究將能找出在延伸基本週期法下ELSPRw的最佳解。本研究的成果將可作為決策者在ELSPRw方面之?考，並提供給產業界實際使用，以改善現場排程的能?。 The Economic Lot Scheduling Problem (ELSP) is concerned with the scheduling of cyclical production of more (n ? 2) than two products on a single facility in equal lots over an infinite planning horizon, assuming stationary and known demands for each product. The objective of the ELSP is to determine the lot size and the schedule of production of each product so as to minimize the average total cost incurred per unit time. The costs considered include the setup cost and inventory holding cost. Recently, researchers pay more attention to the ELSP with reworks (ELSPRw). It has two sources of production: manufacturing of products and rework of detective items. These detective items are generated during manufacturing processes inside a factory. Manufacturing and reworked products compete for the same production facility and therefore must be scheduled simultaneously. The solution methodologies for the ELSP may be divided into two major categories, namely, analytical approaches and heuristics. The analytical approaches include the independent solution (IS) approach, the common cycle (CC) approach, the basic period (BP) approach and the extended basic period (EBP) approach. The IS approach is not able to ensure the feasibility of the production schedule on a facility. The CC approach is a simple method that can guarantee the feasibility of its solution. The solution of the CC approach can be considered as the upper bound of the cost for the ELSP. The CC and BP approaches may be considered as special cases of the EBP approach. Therefore, the EBP approach always obtains the best solution than other approaches. For solving the ELSPRw, there are three key decision variables: the period multipliers of all products, the time length of a basic period and an optimal production sequence. This research provides an procedure that uses the genetic algorithm to solve the ELSPRw under the EBP approach. Our approach will find an optimal or near optimal solution with the lowest average total cost. Our approaches will provide important decision support for production managers at some industries.
