雖然豐田式生產系統 (just-in-time；JIT) 被提出已超過二十餘年的時間，但在現今實務界的生產製造系統中，JIT的概念仍是相當的重要且被廣泛應用。因此在本論文中，我們將討論JIT排程問題中的共同到期日限制下求解總加權提早 (earliness) 及延遲 (tardiness) 最小化之單機排程問題。由於此問題係NP-hard，無最佳化演算法可應用，所以多位學者皆發展各種迭代型演算法如：模擬退火法、基因演算法、塔布搜尋法 (TS) 等，期能在合理的計算時間內得到不錯的結果。因此我們利用變動鄰域搜尋法 (variable neighborhood search；VNS) 結合塔布搜尋法求解此問題。我們提出的VNS/TS演算法具有幾項特色，包括：鄰域間的使用比率、從鄰域中隨機選取五個可行解、B/F局部搜尋法、VNS與TS的結合等。我們將此演算法對標竿問題作測試。實驗結果顯示，VNS/TS演算法不僅在解的品質上有顯著的效果，同時在計算時間方面也有不錯的表現。

Although the concept of just-in-time (JIT) production systems has been proposed for over two decades, it is still important in real-world production systems. In this thesis, we consider minimizing the total weighted earliness and tardiness with a common due date in a single machine environment, which has been proved as an NP-hard problem. Due to the complexity of the problem, metaheuristics, including simulated annealing, genetic algorithm, tabu search, among others, have been proposed for searching good solutions in reasonable computation times. In this thesis, we propose a hybrid metaheuristic that uses tabu search within variable neighborhood search (VNS/TS). There are several distinctive features in the VNS/TS algorithm, including different ratio of the two neighborhoods, generating five points simultaneously in a neighborhood, implementation of the B/F local search, and combination of TS with VNS. By examining the 280 benchmark problem instances, the algorithm shows an excellent performance in not only the solution quality but also the computation time. The results obtained are better than those reported previously in the literature.

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