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題名：塔布搜尋法求解排程問題之研究
作者：廖麗滿
作者(外文)：Liao, Li-Man
校院名稱：國立臺灣科技大學
系所名稱：工業管理系
指導教授：廖慶榮
學位類別：博士
出版日期：2001
主題關鍵詞：共通式演算法；基因演算法；模擬退火法；塔布搜尋法；單機；主要設置時間；次要設置時間；流程型工廠；Meta-heuristic；Genetic Algorithm；Simulated Annealing；Tabu Search；Single Machine；Major Setup；Minor Setup；Flow Shop
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三個尋找接近最佳解之優異技術：基因演算法 (Genetic Algorithm；GA)、模擬退火法 (Simulated Annealing；SA) 及塔布搜尋法 (Tabu Search；TS)，已廣泛地應用在求解組合式最佳化的問題，這些技術通常被稱為共通式演算法 (meta-heuristics) 或一般啟發式演算法 (general heuristics)。本論文首先介紹這三個方法的演算架構及其參數的設定，並且比較其差異性。然後，使用其中的一個方法——塔布搜尋法，提出有效的啟發式演算法，求解兩個排程問題。第一個問題是考量雙重設置時間下之單機排程問題，另一個問題是多重準則之流程型工廠排程問題。
對於第一個問題，我們分別以動態規劃法 (Dynamic Programming；DP) 和塔布搜尋法，發展出兩個啟發式演算法，這兩個演算法在工作個數不大於30個時，皆有良好的演算績效。但是，當問題變大時，以動態規劃法為基礎的演算法所求出的解，經常陷於局部最佳解，故採用TS技術來克服此現象。以 TS 技術為基礎的演算法，使用了區間式的鄰近結構和分散式策略，計算結果顯示，以 TS 技術為基礎的演算法較 DP 所發展的演算法為佳。
對於多目標之流程型工廠排程問題，我們也以TS技術來發展演算法。本研究所考慮的多目標有兩組，一是最大完工時間、總流程時間和機器的總閒置時間；另一是最大完工時間、總流程時間和總延遲時間。求解此問題的 TS 技術之演算法，主要是運用了簡易的鄰近結構，以及加強型與分散式的長期搜尋策略。計算結果顯示，此TS技術之啟發演算法較已發展出的GA演算法為佳。
Three prominent techniques, genetic algorithm (GA), simulated annealing (SA) and tabu search (TS), for finding near optimal solutions to combinatorial optimization problems have been used widely. These techniques are called meta-heuristics or general heuristics. In this research, we first introduce a basic version of each method, give indications about tuning parameters, and compare them. Then we select one of the three methods, tabu search, to solve two scheduling problems. The first problem is a single-machine scheduling problem with major and minor setups; the second problem is a permutation flow-shop scheduling problem with multiple objectives. We propose heuristics based on TS to compare with the existing heuristics.
For the first problem, we apply dynamic programming (DP) and TS to develop two heuristics, respectively. The performances of both heuristics dealing with small- or medium-sized problems are well. But for large-sized problems, the heuristic based on DP often traps in local optimum. Therefore, we try to deploy TS technique to overcome the problem. The TS based heuristic uses block neighborhoods and diversification. According to computational results, the performance of TS based heuristic is superior to the DP based heuristic.
The second problem, flow shop scheduling with multiple objectives, is frequently encountered in practice. We also develop a TS based heuristic to solve the problem. In particular, two problems with triple-criteria are under consideration, i.e., the minimization of makespan, total flow time, and total idle time, and the minimization of makespan, total flow time, and total tardiness. A heuristic based on TS technique is developed for the problems. The heuristic suggests a simple technique for generating neighborhoods of a given sequence and adopts a combined scheme for intensification and diversification, using frequency-based memory to search a new region. Computational results show that the heuristic is better than two existing genetic algorithm based heuristics.
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