臺灣博碩士論文加值系統

傳統的區位與途程的問題，並不考慮物流中心間的轉運特性。而近年來在運輸配送系統的設計上，產生利用合併運輸與轉運方式，透過大量運輸來產生規模經濟效果的軸輻式網路（Hub-and-Spoke Network）設計。因此，結合軸輻式網路特性與區位途程問題的物流決策，乃成為一個有意義的研究課題。
本研究乃以Perl與Daskin（1985）所發展的區位途程模式為基礎，在考慮顧客服務的外部服務網路（亦即供應鏈上下游間的輸配送）外，多加考慮物流中心間的內部營運網路（亦即供應鏈同一層級間的輸配送），建構一個軸輻式物流配送網路系統（O’Kelly，1985），並結合基因演算法與禁忌搜尋法的特性發展一個有效的啟發式演算法。
利用Perl（1983）所測試的九個範例問題，將本（簡化後）演算法與現行其他五種較新的演算法做求解品質的比較，結果發現以程建銘（2001）的基因演算法與本演算法最佳。結果亦發現，在求解較大型的問題時，本演算法的效果較前者好，探究其原因係本研究多了途程內改善的OPT法。
在參數分析中可以發現，當需求點增加時，會使得物流總成本上升；但是當物流中心與供應商數目增加時，因為可以選擇出較好的物流中心與供應商供貨的組合，反而使得物流總成本下降。因此設計一物流配送網路時，不應只考慮物流中心與顧客的區位，應以供應鏈物流網路設計的角度出發，同時考慮供應商、物流中心與顧客三個階層，以獲得最佳物流總成本的設計。

Traditional location-routing problem didn’t take transshipment between same echelons into consideration. However, in the past decade, hub-and-spoke network system has been widely used in a distribution system due to its ability to get scale economy through consolidation and transshipment. Thus, a location-routing problem considering hub-and-spoke network characteristics deserves detailed study.
The research defines the features of the system, first. A mathematical model is then developed. Because the problem is hard to solve in reasonable time, an efficient heuristic combining genetic althorithms and tabu search is designed to solve the problem. Based on test problems from literature (Perl and Daskin, 1985), solution quality of our simplied heuristics is compared with other five research papers. It is found that our heuristcs and Cheng (2001) have the best performance. For large-size problems, our heuristics performs better due to 2-OPT alghoritms in routing moving phase of the heuristic.
From parameter analysis, it is found that logistics cost increases when number of customers increases. However, logistics cost may decrease when the number of DCs and suppliers increases since better locations may be found. It is thus suggested that customers, DCs, and suppliers should be considered in an integral basis when designing a distribution system.

中文摘要i
Abstractii
誌謝iii
目錄iv
表目錄vi
圖目錄vii
第一章 緒論1
1.1 研究背景與動機1
1.2 研究目的3
1.3 研究架構5
1.4 研究範圍與限制7
1.4.1 基本假設7
1.4.2 作業假設8
第二章 文獻回顧9
2.1 軸輻式網路系統9
2.1.1 軸輻式網路系統發展9
2.1.2 軸輻式網路系統型態10
2.1.3 軸心設施型物流中心定義15
2.2 設施區位問題18
2.2.1 傳統設施區位問題18
2.2.2 軸輻式設施區位問題19
2.2.3 軸輻式設施區位與傳統設施區位問題比較22
2.3 區位與途程問題24
2.4 禁忌搜尋法（TABU SEARCH；TS）26
2.4.1 禁忌搜尋法基本模組26
2.4.2 禁忌搜尋法執行架構27
2.5 基因演算法（GENETIC ALGORITHMS；GA）29
2.5.1 基因演算法基本模組30
2.5.2 基因演算法優缺點32
第三章 數學規劃模式與啟發式演算法34
3.1 軸輻式區位途程問題之數學規劃模式34
3.1.1 符號說明38
3.1.2 軸輻式物流中心配送模式40
3.1.3 數學模式說明42
3.2 軸輻式區位途程問題之啟發式演算法44
3.3 演算法初始建構階段-基因演算法的設計45
3.3.1 基因演算法的執行步驟46
3.3.2 軸輻式物流中心區位分派模式演算法51
3.3.3 軸輻式物流中心車輛途程建構模式53
3.4 演算法改善階段-禁忌搜尋法的設計56
3.4.1 移步的決定57
3.4.2 移步值的評估62
3.4.3 禁忌模組的設計64
3.4.4 禁忌搜尋法改善的作業流程68
3.5 影響演算法執行效率的參數分析72
第四章 資料分析75
4.1 啟發式演算法的求解品質測試75
4.2 啟發式演算法的求解品質比較77
4.3 模式參數對於配送決策的影響79
第五章 結論與建議87
5.1 結論與建議87
5.2 未來研究方向89
參考文獻90
中文部分90
英文部分91

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