工作排程問題及維修時機選擇的問題在製造業中經常被廣泛的研究討論,然而卻較少研究同時考慮該兩種問題。在實際生產過程中,工廠的人員往往會面臨上述兩種問題的決策衝突。工廠若長期不維修保養機台,會造成機況下降而經常故障,進而使生產效率降低。反之,維修機台雖然可以使機台在往後更有效率的生產,卻也可能造成實際生產時間被佔用,進而使訂單無法被即時滿足。由此可知,維修機台與否的決策應該同時考慮生產及需求情況,才能有效達成效益最大化。 在本研究中,我們提出了一個混合整數規劃模型來描述這個最佳化問題。由於這是一個 NP-hard 問題,因此並不存在一個有效的演算法能夠在有效時間內找出最佳解。有鑑於此,我們基於禁忌演算法進一步設計了兩種啟發式演算法以在有效時間內得到靠近最佳解的可行解,並透過數值實驗來驗證演算法的功效。最後,我們透過實際合作的電子製造業公司所提供之真實資料,展示如何從實際工廠機台的資料估計出模型之中的良率下降速度。
While job scheduling problems have been studied extensively, scheduling problems with endogenous yield rates that may be affected by predictive maintenance is seldom simultaneously investigated. In practice, a decision maker may find some time to insert predictive maintenance onto some machines. This occupies machines and delay the processing of jobs but increases yield rates to shorten future job processing times. In this study, we consider the optimization a joint predictive maintenance and job scheduling problem for the minimization of total tardiness. A mixed integer programming model is proposed in this study. To solve this NP-hard problem in a reasonable time, we develop two heuristic algorithms based on Tabu search. We evaluate the performance of our algorithms through numerical experiments. Finally, we collaborate with a real electronics manufacturing company and demonstrate one possible way to estimate the yield declining rates, which are required in our model.