傳統上,多數研究以兩段式方式去進行直線直角距離計算的設施佈置與出入口位置配置,但在實務上基於此距離尺度所完成之設施規劃結果較不符合現況。故本研究採用符合實際路徑的距離尺度-輪廓距離去衡量設施間距離,將設施佈置與出入口位置規劃整併,規劃出更實際的設施佈設。 本研究結合蟻群最佳化演算法(Ant Colony Optimization, ACO)與複製選擇演算法(Clonal Selection Algorithm, CSA)發展出一改良式蟻群演算法(Hybrid Ant Colony Optimization, HACO)求解不等面積設施佈置問題暨出入口規劃。HACO係利用CSA特性來增加ACO的起始解品質與蟻群多樣性,強化ACO的搜尋能力。本研究採用數個國際標準測試例題來測試HACO之演算效率,並與相關文獻中的最佳解結果進行比較。經實驗分析後,在部分國際測試例,HACO可求得比目前國際最佳解相同甚至是更好的解且運算效率較ACO佳。
Traditionally, most studies used two-stage approach to solve block layout problem with I/O points design. In order to make planning results more practical and measure distance between facilities more precisely, this thesis integrates block layout problem and I/O points design using a contour distance metric. In this thesis, ant colony optimization (ACO) and clonal selection algorithm (CSA) are combined and a hybrid ant colony algorithm (HACO) is proposed to solve unequal-area facility layout problem with I/O points design. Operations of CSA are embedded in the ACO to improve the solution quality of initial ant solutions and increase differences among ant solutions, so search capability of HACO is enhanced. Several international benchmark problems are used to test the algorithm efficiency of HACO. Compared with preview researches, HACO can obtain the close or better solutions.
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