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系統識別號 U0002-2706201321271500
中文論文名稱 緊急救災供應鏈網路設計與救災物流配送路線規劃-以日本機場緊急供應鏈為例
英文論文名稱 Emergency Supply Chain Network Design and Disaster Relief Logistics Problem-In the Case of Japan Airport Emergency Supply Chain
校院名稱 淡江大學
系所名稱(中) 管理科學學系碩士班
系所名稱(英) Master’s Program, Department of Management Sciences
學年度 101
學期 2
出版年 102
研究生中文姓名 李明軒
研究生英文姓名 Ming-Hsuan Li
學號 600620396
學位類別 碩士
語文別 中文
口試日期 2013-06-29
論文頁數 148頁
口試委員 指導教授-廖述賢
指導教授-謝佳琳
委員-倪衍森
委員-廖衛邦
中文關鍵字 人道救援供應鏈(或緊急供應鏈)  整合性供應鏈  區位定址及路徑問題  多目標基因遺傳演算法  啟發式演算法 
英文關鍵字 Humanitarian Relief Chain  Location-Routing Problem(LRP)  Integrated Supply Chain Model  Multi-objective Evolutionary Algorithm  Macro Heuristic Algorithm 
學科別分類
中文摘要 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。
英文摘要 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.
論文目次 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


參考文獻 一、中文文獻
池昆霖(2006),區位途程與易腐性商品排程之研究,國立中央大學土木工程學系研究所碩士論文,桃園。
吳琴玲(2001),「物流配送系統之區位-途程問題研究」,國立雲林科技大學工業工程學管理研究所碩士論文,雲林。
胡琇涵(2012),整合性庫存控制與配送物流網路之多目標區位定址問題之模型建立與探討-以台灣醫療血液供應鏈為例,淡江大學管理科學研究所碩士論文,台北。
張有恆(1998),物流管理。台北:華泰文化事業公司。
許晉嘉(2003)。宅配業貨物配送路線規劃問題之研究。國立交通大學交通運輸研究所碩士論文,新竹。
許志義(1994),多目標決策互動限制權重法之整合與運用。台北:五南圖書出版公司,台北。
曾建元(2010)。大型量販店整合性庫存控制與銷售物流網路之多目標區位定址問題。淡江大學管理科系碩士班碩士論文,台北。
蔡麗敏(1999)。廢輪胎處理廠區位指派與運送路線選擇之研究。國立交通大學交通運輸研究所碩士論文,新竹。

二、英文文獻
Akkihal, A. R. (2006). Inventory pre-positioning for humanitarian operations. Massachusetts Institute of Technology.
Angelelli, E., & Mansini, R. (2002). The vehicle routing problem with time windows and simultaneous pick-up and delivery Quantitative approaches to distribution logistics and supply chain management (pp. 249-267): Springer.
Balcik, B., & Beamon, B. M. (2008). Facility location in humanitarian relief. International Journal of Logistics, 11(2), 101-121.
Balcik, B., Beamon, B. M., Krejci, C. C., Muramatsu, K. M., & Ramirez, M. (2010). Coordination in humanitarian relief chains: Practices, challenges and opportunities. International Journal of Production Economics, 126(1), 22-34.
Balcik, B., Beamon, B. M., & Smilowitz, K. (2008). Last mile distribution in humanitarian relief. Journal of Intelligent Transportation Systems, 12(2), 51-63.
Barreto, S., Ferreira, C., Paixao, J., & Santos, B. S. (2007). Using clustering analysis in a capacitated location-routing problem. European Journal of Operational Research, 179(3), 968-977.
Beamon, B. M. (1998). Supply chain design and analysis: Models and methods. International journal of production economics, 55(3), 281-294.
Beamon, B. M. (2004). Humanitarian relief chains: issues and challenges. Paper presented at the Proceedings of the 34th International Conference on Computers and Industrial Engineering.
Beamon, B. M., & Balcik, B. (2008). Performance measurement in humanitarian relief chains. International Journal of Public Sector Management, 21(1), 4-25.
Beamon, B. M., & Kotleba, S. A. (2006). Inventory modelling for complex emergencies in humanitarian relief operations. International Journal of Logistics: Research and Applications, 9(1), 1-18.
Beasley, J. E., & Chu, P. C. (1996). A genetic algorithm for the set covering problem. European Journal of Operational Research, 94(2), 392-404.
Benayoun, R., De Montgolfier, J., Tergny, J., & Laritchev, O. (1971). Linear programming with multiple objective functions: Step method (STEM). Mathematical programming, 1(1), 366-375.
Bertazzi, L., Savelsbergh, M., & Speranza, M. G. (2008). Inventory routing The Vehicle Routing Problem: Latest Advances and New Challenges (pp. 49-72): Springer.
Bodin, L., Golden, B., Assad, A., & Ball, M. (1983). Routing and scheduling of vehicles and crews: The state of the art. COMP. & OPER. RES., 10(2), 63-211.
Bramel, J., & Simchi-Levi, D. (1997). The logic of logistics: theory, algorithms, and applications for logistics management: Springer Verlag.
Chao, I., Golden, B. L., & Wasil, E. (1993). A new heuristic for the multi-depot vehicle routing problem that improves upon best-known solutions. American Journal of Mathematical and Management Sciences, 13(3-4), 371-406.
Charnes, A., & Cooper, W. W. (1957). Management models and industrial applications of linear programming. Management Science, 4(1), 38-91.
Chen, G., Li, C., Ye, M., & Wu, J. (2009). An unequal cluster-based routing protocol in wireless sensor networks. Wireless Networks, 15(2), 193-207.
Coello, C. A. (2006). Evolutionary multi-objective optimization: a historical view of the field. Computational Intelligence Magazine, IEEE, 1(1), 28-36.
Cohon, J. L. (1978). Multiobjective programming and planning: Academic Press (New York).
Current, J., Min, H., & Schilling, D. (1990). Multiobjective analysis of facility location decisions. European Journal of Operational Research, 49(3), 295-307.
Current, J., & Weber, C. (1994). Application of facility location modeling constructs to vendor selection problems. European journal of operational research, 76(3), 387-392.
Dantzig, G. B., & Ramser, J. H. (1959). The truck dispatching problem. Management science, 6(1), 80-91.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. Evolutionary Computation, IEEE Transactions on, 6(2), 182-197.
Dethloff, J. (2001). Vehicle routing and reverse logistics: the vehicle routing problem with simultaneous delivery and pick-up. OR-Spektrum, 23(1), 79-96.
Dupont, L. (2008). Branch and bound algorithm for a facility location problem with concave site dependent costs. International Journal of Production Economics, 112(1), 245-254.
Equi, L., Gallo, G., Marziale, S., & Weintraub, A. (1997). A combined transportation and scheduling problem. European Journal of Operational Research, 97(1), 94-104.
Erenguc, S. S., Simpson, N. C., & Vakharia, A. J. (2006). Integrated production/distribution planning in supply chains: An invited review. European journal of operational research, 115(2), 219-236.
Fiedrich, F., Gehbauer, F., & Rickers, U. (2000). Optimized resource allocation for emergency response after earthquake disasters. Safety Science, 35(1), 41-57.
Fonseca, C. M., & Fleming, P. J. (1993). Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization. Paper presented at the Proceedings of the fifth international conference on genetic algorithms.
Geoffrion, A. M., Dyer, J. S., & Feinberg, A. (1972). An interactive approach for multi-criterion optimization, with an application to the operation of an academic department. Management Science, 357-368.
Glover, F. (1977). Heuristics for integer programming using surrogate constraints. Decision Sciences, 8(1), 156-166.
Goldberg, D. E., & Holland, J. H. (1988). Genetic algorithms and machine learning. Machine learning, 3(2), 95-99.
Holland, J. H. (1975). Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence: University of Michigan Press (Ann Arbor).
Horn, J., Nafpliotis, N., & Goldberg, D. E. (1994). A niched Pareto genetic algorithm for multiobjective optimization. Paper presented at the Evolutionary Computation, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the First IEEE Conference on.
Hwang, C. L., & Masud, A. S. M. (1979). Multiple objective decision making-methods and applications (Vol. 164): Springer.
Ijiri, Y. (1965). Management goals and accounting for control (Vol. 3): North Holland Pub. Co.
Kemball‐Cook, D., & Stephenson, R. (1984). Lessons in logistics from Somalia. Disasters, 8(1), 57-66.
Kirkpatrick, S., Jr., D. G., & Vecchi, M. P. (1983). Optimization by simulated annealing. science, 220(4598), 671-680.
Knemeyer, A. M., Zinn, W., & Eroglu, C. (2009). Proactive planning for catastrophic events in supply chains. Journal of Operations Management, 27(2), 141-153.
Knowles, J. D., & Corne, D. W. (2002). Enumeration of Pareto optimal multi-criteria spanning trees–a proof of the incorrectness of Zhou and Gen's proposed algorithm. European journal of operational research, 143(3), 543-547.
Koskosidis, Y. A., Powell, W. B., & Solomon, M. M. (1992). An optimization-based heuristic for vehicle routing and scheduling with soft time window constraints. Transportation Science, 26(2), 69-85.
Liao, S.-H., Hsieh, C.-L., & Lai, P.-J. (2011). An evolutionary approach for multi-objective optimization of the integrated location–inventory distribution network problem in vendor-managed inventory. Expert Systems with Applications, 38(6), 6768-6776.
Lin, S., & Kernighan, B. W. (1973). An effective heuristic algorithm for the traveling-salesman problem. Operations research, 21(2), 498-516.
Maranzana, F. (1964). On the location of supply points to minimize transport costs. OR, 261-270.
Marglin, S. A. (1967). Public investment criteria. CAMBRIDGE, MASSACHUSETTS, THE MIT PRESS, 1967. 103 P.
Melo, M. T., Nickel, S., & Saldanha-Da-Gama, F. (2009). Facility location and supply chain management–A review. European Journal of Operational Research, 196(2), 401-412.
Michalewicz, Z., & Schoenauer, M. (1996). Evolutionary algorithms for constrained parameter optimization problems. Evolutionary computation, 4(1), 1-32.
Min, H., & Zhou, G. (2002). Supply chain modeling: past, present and future. Computers & industrial engineering, 43(1), 231-249.
Miranda, P. A., & Garrido, R. A. (2004). Incorporating inventory control decisions into a strategic distribution network design model with stochastic demand. Transportation Research Part E: Logistics and Transportation Review, 40(3), 183-207.
Nagy, G., & Salhi, S. (2007). Location-routing: Issues, models and methods. European Journal of Operational Research, 177(2), 649-672.
Nikbakhsh, E., & Farahani, R. Z. (2011). Humanitarian Logistics Planning in Disaster Relief Operations. Logistics Operations and Management: Concepts and Models, 291.
Nikbakhsh, E., & Zegordi, S. (2010). A Heuristic Algorithm and a Lower Bound for the Two-Echelon Location-Routing Problem with Soft Time Window Constraints. Scientia Iranica Transaction E: Industrial Engineering, 17(1), 36-47.
Nozick, L. K., & Turnquist, M. A. (1998). Integrating inventory impacts into a fixed-charge model for locating distribution centers. Transportation Research Part E: Logistics and Transportation Review, 34(3), 173-186.
Oliver, J., Neurgaonkar, R., & Cross, L. (1988). A thermodynamic phenomenology for ferroelectric tungsten bronze Sr0. 6Ba0. 4Nb2O6 (SBN: 60). Journal of Applied Physics, 64, 37-47.
Or, I. (1976). Traveling salesman-type combinatorial problems and their relation to the logistics of regional blood banking: Xerox University Microfilms.
Owen, S. H., & Daskin, M. S. (1998). Strategic facility location: A review. European Journal of Operational Research, 111(3), 423-447.
Ozdamar, L., Ekinci, E., & Kucukyazici, B. (2004). Emergency logistics planning in natural disasters. Annals of operations research, 129(1-4), 217-245.
Philip, J. (1972). Algorithms for the vector maximization problem. Mathematical Programming, 2(1), 207-229.
Poister, T. H. (2008). Measuring performance in public and nonprofit organizations: Jossey-Bass.
Rahman, S.-u., & Smith, D. K. (2000). Use of location-allocation models in health service development planning in developing nations. European Journal of Operational Research, 123(3), 437-452.
Rand, G. K. (1976). Methodological choices in depot location studies. Operational Research Quarterly, 241-249.
Russell, T. E. (2005). The humanitarian relief supply chain: analysis of the 2004 South East Asia earthquake and tsunami. Massachusetts Institute of Technology.
Schaffer, J. D. (1985). Multiple objective optimization with vector evaluated genetic algorithms. Paper presented at the Proceedings of the 1st international Conference on Genetic Algorithms.
Shen, Z.-J. M. (2005). A multi-commodity supply chain design problem. IIE Transactions, 37(8), 753-762.
Shen, Z.-J. M., Coullard, C., & Daskin, M. S. (2003). A joint location-inventory model. Transportation Science, 37(1), 40-55.
Shen, Z. (2007). Integrated supply chain design models: a survey and future research directions. Journal of Industrial and Management Optimization, 3(1), 1.
Snyder, L. V., & Daskin, M. S. (2005). Reliability models for facility location: the expected failure cost case. Transportation Science, 39(3), 400-416.
Solomon, M. M., & Desrosiers, J. (1988). Survey Paper—Time Window Constrained Routing and Scheduling Problems. Transportation science, 22(1), 1-13.
Srinivas, N., & Deb, K. (1994). Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary computation, 2(3), 221-248.
Steuer, R. E., & Choo, E.-U. (1983). An interactive weighted Tchebycheff procedure for multiple objective programming. Mathematical programming, 26(3), 326-344.
Thevenaz, C., & Resodihardjo, S. L. (2010). All the best laid plans… conditions impeding proper emergency response. International Journal of Production Economics, 126(1), 7-21.
Thai, V. V., & Grewal, D. (2012). An analysis of the efficiency and competitiveness of Vietnamese port system.
Thangiah, S. R., Osman, I. H., & Sun, T. (1994). Hybrid genetic algorithm, simulated annealing and tabu search methods for vehicle routing problems with time windows. Computer Science Department, Slippery Rock University, Technical Report SRU CpSc-TR-94-27, 69.
Thomas, A., & Mizushima, M. (2005). Logistics training: necessity or luxury? Forced Migration Review, 22(22), 60-61.
Tufekci, S., & Wallace, W. (1998). Emerging area of emergency management and engineering. IEEE Transactions on Engineering Management, 45(2), 103-105.
Tuzun, D., & Burke, L. I. (1999). A two-phase tabu search approach to the location routing problem. European Journal of Operational Research, 116(1), 87-99.
Yi, W., & Ozdamar, L. (2007). A dynamic logistics coordination model for evacuation and support in disaster response activities. European Journal of Operational Research, 179(3), 1177-1193.
Yu, P., & Leitmann, G. (1974). Compromise solutions, domination structures, and Salukvadze's solution. Journal of Optimization Theory and Applications, 13(3), 362-378.
Zadeh, L. A. (1963). On the definition of adaptivity. Proceedings of the IEEE, 51(3), 469-470.
Zelany, M. (1974). A concept of compromise solutions and the method of the displaced ideal. Computers & Operations Research, 1(3), 479-496.
Zitzler, E., Laumanns, M., Thiele, L., Zitzler, E., Zitzler, E., Thiele, L., & Thiele, L. (2001). SPEA2: Improving the strength Pareto evolutionary algorithm: Eidgenossische Technische Hochschule Zurich (ETH), Institut fur Technische Informatik und Kommunikationsnetze (TIK).


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