近年來自然災害的數量及受災害影響的人迅速增加。人道供應鏈（或緊急供應鏈）的目標是迅速提供救難物資至影響的地區，盡可能減輕人類的痛苦和死亡。本研究設計救援網路分派系統為一個區位定址及路徑問題(LRP) ，並將LRP分為兩個階段，策略階段為整合供應鏈管理議題中區為定址與庫存等問題，希望決定地區救災中心(LAC)的數量及位置，包括兩個目標：緊急救難物資總供應鏈成本及災區服務回應率(同時考量成本及時間因素)，並利用階層群集分析法改善指派問題；戰術階段則是車輛路徑規劃問題，由策略階段得知LAC所負責服務的災區後，在緊急救難車輛容量及時間窗限制下決定災後環境的配送路線，此階段考慮單目標總車輛總運輸成本最小。
本研究求解方法如下，策略階段將使用NAGA-II基因遺傳演算法求解所建立之多目標最佳化模型，並經由階層式群集分析法改善指派結果，以MATLAB撰寫程式求解。戰術階段則使用混合啟發式基因遺傳演算法混合2-opt方法求解所建立之目標最佳化模型，最後完成災後車輛路徑規劃任務，以C#、JOPT與MATLAB撰寫程式求解。
本研究以日本為個案進行實驗，說明本研究之模型如何在現實問題中運作，最後，在災前預算限制的情況下，找出10個潛在地區救災中心的實際位置，災後以日本311大地震進行實驗，完成災後車輛路徑規劃最佳化，此研究結果可提供於決策者最適選擇方案。

The number of natural disasters and the people affected by disasters have increased over recent years. The objective of disaster response in the humanitarian relief chain is to rapidly provide relief (emergency food, water, medicine, shelter, and supplies) to areas affected, so as to minimize human suffering and death. Therefore, the design and operation of the relief chain play significant roles in achieving an effective and efficient response. In this project, we consider both facility location and vehicle routing decisions for a humanitarian relief chain (or emergency supply chain) responding to quick-onset disasters.
In this study, we consider both facility location and vehicle routing decisions for a humanitarian relief chain (or emergency supply chain) responding to quick-onset disasters. We are going to design a humanitarian relief network and its distribution system. This problem can be considered as a location-routing problem (LRP) which appears as a combination of two difficult problems: the facility location problem (FLP) and the vehicle routing problem (VRP). In this work, we consider a discrete LRP with two levels: a set of potential capacitated local authority centers (LAC) and a set of ordered customers (or victims). In terms of strategic plan, we start with a strategic overview to determine the number and the set of LACs to be installed in a humanitarian relief network. In this stage, we consider facility location and inventory issues and two conflict objectives: relief chain cost and responsiveness level of emergency supplies. In addition, a cluster analysis procedure is incorporated into a sequential heuristic to enhance the facility assignment. In terms of tactic plan, we discuss a vehicle routing problem where each LAC will be responsible for some of the disaster areas determined by the former strategic plan. Every distribution route of emergency vehicles (EVs), designated to a specific LAC (starting and ending at the LAC), will be determined when a disaster is encountered. The problem is also restricted to the capacities of the vehicles and the victims’ response time (time windows) on the disaster areas. Here, we intend to minimize the vehicle routing cost in an effective and timely manner.
Our two-level problem is difficult to solve by some existing optimization algorithms due to the considerable number of decision variables and constraints resulting from the integration. Therefore, in the stage of the strategic plan, we use a hybrid multi-objective evolutionary approach based on a well-known NAGA-II evolutionary algorithm to solve the proposed multi-objective model, and in the same time, we also use cluster analysis in a sequential heuristic to improve LAC assignment. We use the software of MATLAB to solve the proposed multi-objective evolutionary algorithm for the facility location problem. However, in the stage of the tactic plan,　we use a hybrid evolutionary approach based on a well-known GA evolutionary algorithm and 2-opt routes improve algorithm to solve the proposed vehicle-routing  model. In this stage, C#, JOPT as well as MATLAB are used to solve this model.
Finally, computational experiments are conducted to illustrate how these proposed models make effects. We use the case of Japan 311 Earthquake as a realistic case study for post-disaster scenario. The results indicate that we have chosen 10 LACs to be installed due to pre-disaster budget constraints. At the same time, the decision maker could find out optimal solutions for the post-disaster vehicle routing optimization by our proposed methodology.

目錄
目錄	I
圖目錄	III
表目錄	V
第一章緒論	1
1.1 研究背景與動機	1
1.2 研究目的	4
1.3 研究範圍與限制	6
1.4 研究流程與架構	7
第二章文獻探討	9
2.1 研究問題文獻	9
2.1.1災害	9
2.1.2緊急供應鏈管理	11
2.1.3緊急供應鏈模式	13
2.1.4整合供應鏈模式	19
2.1.5設施區位定址與車輛路徑規劃問題	22
2.2研究方法文獻	30
2.2.1多目標規劃法	30
2.2.2群集指派法	46
2.2.3車輛路徑規劃法	47
2.3小結	50
第三章緊急供應鏈模式建構	50
3.1研究問題描述	51
3.2研究問題建構	55
3.2.1策略階段設址研究假設	55
3.2.2策略階段設址模型符號說明	55
3.2.3策略階段設址模型	57
3.2.4整合性緊急物流網路之多目標設址模型	61
3.2.5戰術階段車輛路徑規劃研究假設	62
3.2.6戰術階段車輛路徑規劃模型符號說明	63
3.2.7戰術階段車輛路徑規劃模型	64
3.2.8整合性緊急物流網路之車輛路徑規劃模型。	65
第四章問題求解方法	66
4.1基因遺傳演算法	66
4.2多目標基因遺傳演算法	67
4.3第二代非支配基因演算法(NSGA-II)	67
4.4以多目標基因遺傳演算法求解策略階段選址模型	70
4.5以階層群集分析法優化策略階段選址後指派結果	75
4.6以巨集啟發式基因遺傳演算法求解戰術階段車輛路徑規劃模型	76
4.7小結	81
第五章個案設計與分析	82
5.1 個案設計說明	82
5.1.1策略階段相關資料論述與模型符號假設說明	84
5.1.2戰術階段相關資料論述與模型符號假設說明	90
5.2 個案求解與分析	95
5.2.1 策略階段區位定址求解與分析	95
5.2.2 戰術階段車輛路經規劃求解與分析	107
第六章結論與後續研究建議	130
6.1研究結論	130
6.2管理意涵	132
6.2.1學術管理意涵	132
6.2.2實務管理意涵	133
6.3後續研究建議	137
6.3.1資料蒐集	137
6.3.2研究方法	137
6.3.3緊急供應鏈調撥與管理系統	138
6.3.4整合性緊急供應鏈管理	138
參考文獻	139
一、中文文獻	139
二、英文文獻	140
附錄	147
附錄一：NGDC日本1900年至2012年自然災害發生地點資料	147
附錄二：日本國土交通省統計311東北大地震救難資料	148
 
圖目錄
圖1-1目前日本救災物資供應模式	3
圖1-2緊急供應鏈物流結構	4
圖1-3研究流程	7
圖2-11900-2005年不同種類自然災害出現的次數	10
圖2-2自然災害波動與文明能力吸收範圍	10
圖2-3一般商業供應鏈模式	15
圖2-4災害管理物流模式	16
圖2-5緊急供應鏈物流結構	17
圖2-6整合性供應鏈模式	20
圖2-7整合性供應鏈模式	20
圖2-8LRP單階段問題	29
圖2-9LRP雙階段問題	29
圖2-10柏拉圖最適解集合	33
圖2-11傳統多目標規劃法	34
圖2-12多目標遺傳演算法分類圖	38
圖3-1本研究緊急供應鏈物流結構	53
圖3-2本研究策略定址設計和戰術路徑定址設計流程	54
圖3-3本研究災區服務回應率範例	59
圖4-1非支配解排序過程	68
圖4-2NSGA II的演算流程	69
圖4-3策略選址階段問題之基因編碼方式	70
圖4-4混合式基因遺傳演算流程圖	72
圖4-5均勻多點交配的範例說明	73
圖4-6車輛路徑規劃之機因編碼方式	76
圖4-7巨集啟發式基因遺傳演算法流程圖	77
圖4-8起始解建構範例	78
圖4-9順序交配法範例說明	79
圖4-10突變範例說明	80
圖4-11路線改善範例說明	80
圖5-1研究個案策略階段之緊急物流配送網絡架構圖	83
圖5-2研究個案戰術階段之緊急物流配送網絡架構圖	83
圖5-3災害未發生與已發生影響機率	86
圖5-4以岩手縣為例第一日預估需求量	91
圖5-5戰術階段災區位置	91
圖5-6情境1：車輛數與總Routing距離關係	108
圖5-7情境1：車輛數與總Routing總時間關係	108
圖5-8情境2：車輛數與總Routing距離關係	120
圖5-9情境2：車輛數與總Routing總時間關係	120
圖6-1設置地區救災中心前	134
圖6-2設置地區救災中心後	134
圖6-3車輛最佳路徑規劃前	136
圖6-4車輛最佳路徑規劃後	136

 
表目錄
表2-1一般供應鏈與緊急供應鏈績效衡量的特性	11
表2-2緊急供應鏈績效衡量指標總表	13
表2-3近年專家學者對緊急供應鏈研究	14
表2-4TSP與VRP之比較表	24
表2-5旅行銷售員問題之分類	25
表2-6LRP問題分類	28
表2-7多目標規劃法相關研究文獻整理	31
表2-8傳統多目標最佳化方法	35
表2-9多目標遺傳演算法分類表	42
表4-1典型的基因遺傳演算法架構	66
表4-2兩種貪婪法則說明	71
表4-3階層群集分析法群間距離計算方法	75
表5-1案例之中央調度中心	84
表5-2案例之地區救災中心	84
表5-3案例之災區	85
表5-4範例:日本5都道縣府自然災害影響次數	86
表5-5範例:自然災害影響相對權重	86
表5-6範例:日本5都道縣府自然災害影響次數＊相對權重	86
表5-7範例:日本5都道縣府災區預期需求量	87
表5-8相關參數假設資料說明	87
表5-9日本國土交通省救難物資(水)統計資料	90
表5-10車輛能容納600ml24入飲用水數量	92
表5-11各災區區域時間窗	92
表5-12相關參數假設資料說明	93
表5-13柏拉圖最適解之演進趨勢圖與圖形說明	95
表5-14柏拉圖前緣解與計算決策結果	97
表5-15目標函數值與地區救災中心設置數	97
表5-16柏拉圖最適解與對應參數	98
表5-17策略選址前LAC數量與位置示意圖	99
表5-18策略選址後LAC數量與位置示意圖	100
表5-19策略選址階段指派結果	102
表5-20階層分析法集群優化指派結果	103
表5-21階層分析法集群切點結果	104
表5-22階層分析法集群切點與合併對應參數	105
表5-23階層分析法集群合併結果	106
表5-23情境1：車輛數量5至15台求解最佳解結果	107
表5-24情境1：最佳解目標函數之車輛數量	109
表5-24情境1：決策後最佳解各路徑對應時間及距離	109
表5-25情境1：決策後最佳解各路徑對應參數	110
表5-26情境1：路徑1、2示意圖	115
表5-27情境1：路徑3、4示意圖	116
表5-28情境1：路徑5、6示意圖	117
表5-29情境1：路徑7、8示意圖	118
表5-30情境2：車輛數量5至15台求解最佳解結果	119
表5-31情境2：最佳解目標函數之車輛數量	121
表5-32情境2：決策後最佳解各路徑對應時間及距離	121
表5-33情境2：決策後最佳解各路徑對應參數	122
表5-34情境2：路徑1、2示意圖	125
表5-35情境2：路徑3、4示意圖	126
表5-36情境2：路徑5、6示意圖	127
表5-37情境2：路徑7、8示意圖	128
表5-38情境1與情境2最佳解比較表	129

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