最近幾年,有重製狀況的經濟批量排程問題(economic lot scheduling problem with reworks, ELSPR)之研究日漸受到重視。ELSPR是假設一產品的製造批量生產完畢後,會有一部份因爲瑕疵等因素而必須進行修理(repair)或重加工(rework)。ELSPR的成本有兩個:一個是理想成本(ideal cost),包括整置成本和存貨持有成本等,一是額外成本(additional cost),包括因修復或重製之時間差而導致的額外存貨持有成本。因此,求解ELSPR的目標是最小化理想成本和額外成本的總和。當產品的參數是已知且固定,則理想成本是一固定的數值,而額外成本則與產品之批量生產順序有密切關係。本研究提出在共同週期法下ELSPR之數學模式,並假設在一週期內一產品僅有一個製造批量和一個重製批量。本研究建議一個混合式遺傳演算法來求解ELSPR之最佳批量生產順序,以最小化額外成本。本研究的數值範例顯示遺傳演算法的求解效率,得到的成本最多僅比最佳解高出0.18%。
In this study, we are interested in the economic lot scheduling problem (ELSP) with reworks in which the production system deals with two sources of products: manufacturing of the serviceable products and remanufacturing of the rework products. We may consider that these two categories of products compete for the same facility and they must be scheduled simultaneously. In this paper, we formulate a mathematical model for the ELSP with reworks using the common cycle (CC) approach in which only one manufacturing lot and only one rework lot for each product exist during a common cycle, and all the products share the same replenishment cycle. In order to solve this problem, we propose a hybrid genetic algorithm (GA) approach that obtains an optimal production sequence of all manufacturing and rework lots with the lowest average total cost. Our numerical examples demonstrate the effectiveness of the proposed hybrid GA.