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研究生:楊家豪
研究生(外文):Chai-hao Yang
論文名稱:應用模糊分群技術於需求導向之物流配送問題之研究
論文名稱(外文):A Study of Demand-Oriented Logistics Using Fuzzy Clustering Technology
指導教授:廖彩雲廖彩雲引用關係
指導教授(外文):Tsai-Yun Liao
學位類別:碩士
校院名稱:朝陽科技大學
系所名稱:資訊管理系碩士班
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2006
畢業學年度:94
語文別:中文
論文頁數:92
中文關鍵詞:車輛巡迴路線問題線上型車輛路線問題Fuzzy c-mean模糊分群禁制搜尋法
外文關鍵詞:Vehicle routing problem with time windowsTabu searchOn-line vehicle routing problemsFuzzy c-meanFuzzy clustering
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隨著企業經營環境愈來愈複雜,市場的競爭愈來愈激烈,企業想要在這樣的經營環境下佔有一席之地並維持競爭與發展,提昇管理決策品質與貼心的服務來滿足顧客需求,是成功經營必要之方法。而在顧客導向的競爭環境中,企業必須以更有效的方法來配送商品以達到更高的顧客滿意度。物流配送若只考慮顧客的地理位置、開關門時間與車容量的限制,並不能符合目前以客為尊、顧客需求導向的市場型態。因此本研究利用混合式模糊階層分群的概念,將顧客的配送需求屬性如服務品質、地理位置、配送時間與貨物體積等,模糊化後再分群,將需求屬性類似者分群後,再規劃其車輛巡迴路線,以達最佳配送之目標。如此不單只是重視某幾個特定屬性,而是考慮每個顧客的屬性,達到重視顧客需求的效果。
此外,當車輛在進行配送時,如何因應顧客即時性需求的產生,以滿足顧客之需求亦是物流配送之一重要議題。所以本研究在解決線上型車輛路線問題(on-line vehicle routing problem)時,若有即時性需求產生時將以顧客地理位置進行fuzzy c-mean重新分群或調整分群結構,完成顧客分群後以成本函數計算出配送路徑之初始解,並藉由禁制搜尋法(tabu search)改善路徑進而獲得配送路徑之趨近最佳解,將調整後的路線提供給正在進行配送之車輛,達到以最節省成本之方式完成需求導向之物流配送。
Commercial Vehicle Operations (CVO), one of the major Intelligent Transportation Systems (ITS) program, aims at applying advanced technologies to commercial vehicle operations. Vehicle Routing Problem with Time Windows (VRPTW) is an important aspect of CVO and logistical distribution problems. Due to the growing complexity of customer demand attributes, the logistical distribution operations must consider not only the time windows and customer locations but also the other demand attributes such as service, quality, and delivery conditions. This research focuses on using fuzzy clustering and fuzzy c-mean technologies to cluster customers and tabu search (TS) algorithms to improve vehicle routing in the logistical distribution operations. Fuzzy clustering is employed to cluster customers considering various demand attributes of customer before solving the initialization phase of vehicle routing. Fuzzy c-mean is used to adjust the clusters and solve the on-line vehicle routing problems (OLVRP). Tabu search is coped to improve the vehicle routing after the initialization phase of an insertion heuristic. Experimental results from numerical studies are presented to show the effectiveness of the proposed methodology and algorithms.
摘要 I
Abstract II
誌謝 III
目錄 IV
圖目錄 VI
表目錄 VII
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 研究目的 4
1.4 研究範圍 5
1.5 研究流程 8
第二章 文獻探討 10
2.1 物流配送之決策屬性 10
2.2 車輛巡迴路線相關問題及相關的求解方法 13
2.2.1 車輛巡迴路線相關問題 13
2.2.2 車輛巡迴路線問題之求解方法 16
2.3 動態車輛巡迴路線相關問題 20
2.4 分群技術 26
2.5 模糊分群方法與應用 32
2.5.1 模糊理論 32
2.5.2 模糊分群方法 33
2.5.3 模糊分群應用 36
第三章 研究方法 38
3.1 研究架構 38
3.2 顧客需求導向物流配送之研究方法 41
3.2.1 物流配送屬性分析 41
3.2.2 模糊階層分群 46
3.2.3 車輛巡迴路徑 51
3.3 即時需求導向之物流配送 54
第四章 實驗結果與分析 60
4.1 顧客需求導向物流配送實驗結果 60
4.1.1 顧客需求屬性分群結果與路徑初始解 61
4.1.2 應用禁制搜尋法求得改善解與配送策略之分析 67
4.2 即時需求導向物流配送實驗結果 73
4.2.1 動態FCM分群結果與初始解 73
4.2.2 應用禁制搜尋法求得即時需求導向物流配送之改善解 76
第五章 結論與建議 78
5.1 結論 78
5.2 建議 79
參考文獻 80
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