Adopting co-evolution and constraint-satisfaction concept on genetic algorithms to solve supply chain network design problems
學年 99
學期 1
出版（發表）日期 2010-10-01
作品名稱 Adopting co-evolution and constraint-satisfaction concept on genetic algorithms to solve supply chain network design problems
作品名稱（其他語言）
著者 Chang, Ying-Hua
單位 淡江大學資訊管理學系
出版者 Kidlington: Pergamon
著錄名稱、卷期、頁數 Expert Systems with Applications 37(10), pp.6919–6930
摘要 With the rapid globalization of markets, integrating supply chain technology has become increasingly complex. That is, most supply chains are no longer limited to a particular region. Because the numbers of branch nodes of supply chains have increased, products and raw materials vary and resource constraints differ. Thus, integrating planning mechanisms should include the capacity to respond to change. In the past, mathematical programming and a general heuristics algorithm were used to solve globalized supply chain network design problems. When mathematical programming is used to solve a problem and the number of decision variables is too high or constraint conditions are too complex, computation time is long, resulting in low efficiency, and can easily become trapped in partial optimum solution. When a general heuristics algorithm is used and the number of variables and constraints is too high, the degree of complexity increases. This usually results in an inability of people to think about resource constraints of enterprises and obtain an optimum solution.
 Therefore, this study uses genetic algorithms with optimum search features. This work combines the co-evolutionary mode, which is in accordance with various criteria and evolves dynamically, and constraint-satisfaction mode capacity to narrow the search space, which helps in finding rapidly a solution that, solves supply chain integration network design problems. Additionally, via mathematical programming, a simple genetic algorithm, co-evolutionary genetic algorithm, constraint-satisfaction genetic algorithm and co-evolutionary constraint genetic algorithm are used to compare the experiments result and processing time to confirm the performance of the proposed method. 由於全球化快速發展，供應鏈整合技術已日趨複雜，供應鏈的範圍不再僅限制於特別的區域，隨著供應鏈網路上據點數增加，產品和物料種類繁多與資源限制不同等影響，整合規劃機制須鉅被優秀能力來因應目前的變化。過去解決全球化供應鏈網路設計問題，常用數學規劃法和啟發式演算法，若採用數學規劃法來解，一旦決策變數太多或限制條件過於複雜，其計算時間將費時且導致效率不佳，易陷入區域最佳解。若使用一般的啟發式演算法求解，則當求解變數和限制條件過多時，複雜度將大為提高，無法同時考量周全企業資源的限制和獲得最佳解。
 因此，本研究利用遺傳演算法最佳化搜尋特性，結合可隨不同準則而動態演化的共演化模式，和能夠縮小解答搜尋空間的限制滿足模式，以幫助解決全球供應鏈整合網路設計問題。此外，經由數學規劃法、一般遺傳演算法、共演化是遺傳演算法、限制滿足遺傳演算法和共演化限制滿足式遺傳演算法，做結果與求解時間比較，以驗證本研究所提方法的效能。
關鍵字 Supply chain network design;Genetic algorithms;Co-evolution concept;Constraint-satisfaction concept; Optimization 供應鏈網路設計; 遺傳演算法; 共演化概念; 限制滿足概念; 最佳化
語言 en
ISSN 0957-4174
期刊性質 國外
收錄於 SCI
產學合作
通訊作者 Chang, Ying-Hua
審稿制度 是
國別 GBR
公開徵稿
出版型式 紙本
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