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題 名 | 以自我學習神經網路解三維指派問題=Solving 3-D Assignment Problem Using Self-learning Neural Network |
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作 者 | 楊秉蒼; 呂淑鈴; 葉怡成; | 書刊名 | 電腦學刊 |
卷 期 | 13:3 2001.09[民90.09] |
頁 次 | 頁38-46 |
分類號 | 312.2 |
關鍵詞 | 三維指派問題; 組合最佳化; 類神經網路; 自找學習神經網路; 鄰近搜索演算法; 3-D assignment problem; Combinatorial optimization; Artificial neural networks; Neighbor search algorithms; |
語 文 | 中文(Chinese) |
中文摘要 | 本研究擬以自我學習神經網路混合鄰近搜索演算法解三維指派問題。傳統作業研 究領域對於指派問題的探討多限定於二維型態,目前此類問題已有高效率匈牙利法可求得最 佳解。但問題更深一層探討多維度指派問題時,匈牙利法便不再適用。由於多維度指派問題 已被證實為 NP-Complete 的問題,因此尋求更具效率的演算工具有其必要性。 在此,本文 嘗試以自我學習神經網路混合鄰近搜索演算法解此問題,並與隨機合法解及鄰近搜索演算法 作比較,由測試結果證明本法其有優異的問題求解能力 |
英文摘要 | The purpose of this study is to solve 3-dimension assignment problem by combining self-learning neural networks and neighbor search algorithms. Traditiona operation research is limited in solving 2-dimension assignment problem. It has been proved that the optimal solution of 2-D assignment problem can be efficiently gotten by Hungarian method. However, Hungarian method is not suitable for multi-dimension assignment problem in further discussion. Because multi-dimension problem has been proved as a NP-complete problem, more efficient method is required. This paper solves the problem by combining self-leaning neural networks and neighbor seach algorithms and compares the results with random search algorithm and neighbor search algorithm. The test shows that this method is superior to the two algorithms in solving the problem. |
本系統中英文摘要資訊取自各篇刊載內容。