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本研究將探討非等效平行機台問題中具備順序相依整備時間及不同開始工作時間（Unequal ready-time）之情況，並以最小化總延遲工件權重數為目標值，其目的在改善非等效平行機台問題應用於實際產業中製造環境裡所面對的各項挑戰，如印刷電路板的鑽孔和半導體的測試製程。因本研究欲求解之問題是屬於NP - Hard problems 性質之尋優問題，故利用啟發式方法(heuristics)求解為合適的選擇。此外，本研究計畫開發混合式巨集啟發式方法來求解非等效平行機台問題，主要以禁忌搜尋法為主，在鄰域的搜尋上，也藉由變動鄰域尋優法能夠透過鄰域轉換的機制，進而找出更多好的解。由於啟發式方法對於尋優問題常需花費許多時間來計算才能獲得更好的解，為確保增進求解效率與品質，將針對問題特性開發數種初始解產生法，並也嘗試定義幾個能夠減少尋找鄰近解之鄰域。在後續求解改善的過程中，主要整合變動鄰域(VND)及禁忌(TS)巨集啟發式演算法搜尋最佳解。此外，為了評估本文推導之演算法效能，本研究利用設定之條件隨機產生適量模擬現場狀況的測試情境，進而比較本研究所提出之混合式巨集啟發式方法及標準禁忌搜尋法在不同情境下之表現。 / The problem considered in this paper is a set of independent jobs on unrelated parallel machines with sequence-dependent setup times and with unequal ready times so as to minimize total weighted tardy jobs. These problems can be found in real-life manufacturing environments, such as PCB fabrication drilling operations and semiconductor wafer manufacturing dicing. Since the problems are NP-hard in the strong sense, heuristics are an acceptable practice to finding good solutions. A hybrid meta-heuristics are proposed to solve this scheduling problem. The proposed heuristics belong to a type of solution improvement heuristic; therefore, the heuristics start with an effective initial feasible solution then a meta-heuristic is applied to improve the solution. To enhance both the efficiency and efficacy of the heuristics, several different initial solution generators, based on the characteristics of problems, are developed. The meta-heuristic is a hybrid heuristic integrating the principles of Variable Neighborhood Descent approach (VND) and Tabu Search (TS). In order to evaluate the performance of the proposed heuristics, two sets of large number test scenarios will be designed to simulate practical shop floor problems. Computational experiments will be performed to compare the performance of the proposed heuristics, and a basic tabu search algorithm. The results show the proposed heuristic perform better than the basic tabu search algorithm.

In today's competitive environment, the importance of continuous production, quality improvement, and fast delivery has forced production and delivery processes to become highly reliable. Keeping equipment in good condition through maintenance activities can ensure a more reliable system. However, maintenance leads to temporary reduction in capacity that could otherwise be utilized for production. Therefore, the coordination of maintenance and production is important to guarantee good system performance. The central thesis of this dissertation is that integrating maintenance and production decisions increases efficiency by ensuring high quality production, effective resource utilization, and on-time deliveries. Firstly, we study the problem of integrated maintenance and production planning where machines are preventively maintained in the context of a periodic review production system with uncertain yield. Our goal is to provide insight into the optimal maintenance policy, increasing the number of finished products. Specifically, we prove the conditions that guarantee the optimal maintenance policy has a threshold type. Secondly, we address the problem of integrated maintenance planning and production scheduling where machines are correctively maintained in the context of a dynamic aircraft repair shop. To solve the problem, we view the dynamic repair shop as successive static repair scheduling sub-problems over shorter periods. Our results show that the approach that uses logic-based Benders decomposition to solve the static sub-problems, schedules over longer horizon, and quickly adjusts the schedule increases the utilization of aircraft in the long term. Finally, we tackle the problem of integrated maintenance planning and production scheduling where machines are preventively maintained in the context of a multi-machine production system. Depending on the deterioration process of machines, we design decomposed techniques that deal with the stochastic and combinatorial challenges in different, coupled stages. Our results demonstrate that the integrated approaches decrease the total maintenance and lost production cost, maximizing the on-time deliveries. We also prove sufficient conditions that guarantee the monotonicity of the optimal maintenance policy in both machine state and the number of customer orders. Within these three contexts, this dissertation demonstrates that the integrated maintenance and production decision-making increases the process efficiency to produce high quality products in a timely manner.