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    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/53429

    Title: 開發新的蟻群演算法求解流程式排程問題
    Other Titles: Revised Ant Colony Optimization for Permutation Flow Shop Scheduling
    Authors: 陳春龍
    Contributors: 國立政治大學資訊管理學系
    Keywords: 蟻群演算法;流程式排程問題;總完成時間;啟發式方法
    Date: 2011
    Issue Date: 2012-08-30 15:49:03 (UTC+8)
    Abstract: 本研究擬開發一個新的蟻群演算法來求解常見的流程式排程問題。我們首先開發一個新的費洛蒙 (pheromone) 計算方法來導引蟻群的搜尋機制,然後,我們開發一個新的過濾策略(filter strategy)來預防蟻群的搜尋陷入區域最佳解。如果蟻群的搜尋還是陷入區域最佳解,則我們開發一個新的跳出策略(jumping strategy)來幫助蟻群的搜尋跳出區域最佳解。我們已經使用常用的Taillard 測試問題組來評估新的費洛蒙計算方法、過濾策略和跳出策略的效果,接下來,本研究將使用兩個新的想法完整探討上述的策略對新的蟻群演算法在廣度搜尋能力(explorative capability)與深度搜尋能力(exploitative capability)的影響,以持續改善新的蟻群演算法求解流程式排程問題的效率與效能。
    This research proposes a Revised Ant Colony Optimization (RACO) for the minimization of makespan in permutation flow shop scheduling problems (PFSP-makespan). The proposed algorithm develops a new method to produce pheromone trails for guiding the search and applies a novel filter strategy to keep the search from trapping into local optimums. Also, a new jumping strategy is developed to help the search escape if the search does become trapped at a local optimum. Computational experiments on the well-known Taillard's benchmark data sets have been performed to evaluate the effects of the pheromone trails updating mechanism, the filter strategy and the jumping strategy. A couple of new ideas will be further applied to thoroughly study the impact of the aforementioned strategies on the explorative capability and the exploitative capability of RACO in order to improve its performance for solving PFSP-makespan.
    Relation: 應用研究
    研究期間:10008~ 10107
    Data Type: report
    Appears in Collections:[資訊管理學系] 國科會研究計畫

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