在緊急災難發生時，為了能夠即時且有效率的將救援物資送到災民的手上，本篇論文提出了一個”基於貪婪搜尋的多目標基因演算法”，能夠妥善安排所有可以分配的資源，並且自動產生許多種可用的緊急救援配送排程表提供給決策者。這個演算法結合了貪婪法則的局部搜索能力跟多目標基因演算法的多樣性等特性，使用Google Map來繪製各物資需求點與物資供應站的可行路線，再採用Dijkstra的最短路徑演算法找出需求點與供應站彼此間的最短路徑，並能夠機動性的根據各物資需求點的需要，自動規劃來自多個物資供應站的救援物資配送排程表，又採取NSGAII的方法進行排名與排序處理程序來找出落在沒有被控制支配的Pareto 前線，並且極力降低：未送達的救援物資需求、配送時間、交通花費等三項目標，也預設三種目標的優先順序即是依照這三項的排序做為產生緊急救援配送排程表的決策依據。
我們將這個演算法套用到臺灣著名的集集大地震來檢驗其效能，從模擬實驗結果中，證實所提出的演算法在有限車輛與無限車輛兩項實驗中，可以在10,000代的演化參數設定與平均情況下比多目標基因演算法和傳統貪婪演算法在配送時間的目標上分別提升56.16%與64.11%的效能。從最後的交通工具運送繪圖中，也能更清楚的知道延伸應用的規劃更全面性的提升”基於貪婪搜尋的多目標基因演算法”應用到緊急物流排程問題中。另外我們也評估不同基因交配方法對效能影響，結果顯示Order based方法對整體演算精確度有提升；我們也以暴力法來確認GSMOGA的可靠程度。

To enable the immediate and efficient dispatch of relief to victims of disaster, this thesis proposes a greedy-search-based multi-objective genetic algorithm (GSMOGA) that is capable of regulating the distribution of available resources and automatically generating a variety of feasible emergency logistics schedules for decision-makers. The proposed algorithm merges the features of local search ability of the greedy method and the diversity of multi-objective genetic algorithm to enhance local search speed and diversity explore ability. It uses the Google Map to draw up the available roads which connect the demand points and supply points and applies the Dijkstra algorithm to find the shortest path between each demand point and supply point. It also dynamically adjust distribution schedules from various supply points according to the requirements at demand points, and adopts the NSGAII method to perform rank & sort procedure to find the feasible solution schedules on non-dominated Pareto front in order to minimize the following: unsatisfied demand for resources, time to delivery, and transportation costs. The sequence of three objectives are also applied to be the priority sequence to generate and order routing schedules for the decision maker. The algorithm uses the case of the Chi-Chi earthquake in Taiwan to verify its performance. Simulation results demonstrate that with a limited and unlimited number of available vehicles, the proposed algorithm outperforms the multi-objective genetic algorithm (MOGA) and the standard greedy algorithms in ‘time to delivery’ by 56.16% and 64.11%, respectively under the 10,000 generations and average situation. The final routing figures show that the GSMOGA is more comprehensive in the emergency logistics scheduling problem. We study the effect of different crossover methods on the performance of GSMOGA. The results show that order based crossover performs the best. We verify the correctness of GSMOGA by comparing the result using the brute force approach.

論文審定書 i
誌謝 ii
中文摘要 iv
Abstract v
Chapter 1 Introduction 1
1.1. Background and motivation 1
1.2. Contributions 3
1.3. Thesis organization 3
Chapter 2 Review of Related Works 5
2.1. Review of preparedness phase 10
2.2. Review of response and recovery phase 10
Chapter 3 Problem Definition 13
3.1. Notations 13
3.2. Problem formulation 16
3.2.1. Problem model 16
3.2.2. Objective functions 18
Chapter 4 The Proposed Algorithm 22
4.1. Data input 23
4.2. Initialization 23
4.3. Greedy-search-based multi-objectives GA 26
Chapter 5 Simulation 33
5.1. Benchmark school29 simulation 33
5.2. Comparisons of different crossover methods 41
5.3. Limited number of transportations 44
5.4. Unlimited number of transportations 48
5.5. Comparison with ground truth using the brute-force method 50
5.6. Summary of simulation 51
Chapter 6 Conclusions and Future Works 58
Bibliography 61
Appendix 68

[1] B. Eksioglu, A. V. Vural, and A. Reisman, "The vehicle routing problem: A taxonomic review," Computers & Industrial Engineering, vol. 57, pp. 1472-1483, 2009.
[2] J.-B. Sheu, "An emergency logistics distribution approach for quick response to urgent relief demand in disasters," Transportation Research Part E: Logistics and Transportation Review, vol. 43, pp. 687-709, 2007.
[3] F. Liberatore, M. T. Ortuño, G. Tirado, B. Vitoriano, and M. P. Scaparra, "A hierarchical compromise model for the joint optimization of recovery operations and distribution of emergency goods in Humanitarian Logistics," Computers & Operations Research, vol. 42, pp. 3-13, 2014.
[4] B. Vitoriano, M. T. Ortuño, G. Tirado, and J. Montero, "A multi-criteria optimization model for humanitarian aid distribution," Journal of Global Optimization, vol. 51, pp. 189-208, 2010.
[5] Y. Yuan and D. Wang, "Path selection model and algorithm for emergency logistics management," Computers & Industrial Engineering, vol. 56, pp. 1081-1094, 2009.
[6] K. G. Zografos, K. N. Androutsopoulos, and G. M. Vasilakis, "A real-time decision support system for roadway network incident response logistics," Transportation Research Part C: Emerging Technologies, vol. 10, pp. 1-18, 2 2002.
[7] J.-Z. Peng, W.-S. Xu, and J.-J. Yang, "A hybrid heuristic algorithm for large scale emergency logistics," in Intelligent Computation Technology and Automation, 2009. ICICTA '09. Second International Conference on, 2009, pp. 899-902.
[8] L. Yang, B. F. Jones, and S.-H. Yang, "A fuzzy multi-objective programming for optimization of fire station locations through genetic algorithms," European Journal of Operational Research, vol. 181, pp. 903-915, 2007.
[9] W. Yi and A. Kumar, "Ant colony optimization for disaster relief operations," Transportation Research Part E: Logistics and Transportation Review, vol. 43, pp. 660-672, 2007.
[10] H. Wang, L. Du, and S. Ma, "Multi-objective open location-routing model with split delivery for optimized relief distribution in post-earthquake," Transportation Research Part E: Logistics and Transportation Review, vol. 69, pp. 160-179, 2014.
[11] Z.-H. Hu, "A container multimodal transportation scheduling approach based on immune affinity model for emergency relief," Expert Systems with Applications, vol. 38, pp. 2632-2639, 2011.
[12] L. Özdamar and Y. Wei, "Greedy neighborhood search for disaster relief and evacuation logistics," Intelligent Systems, IEEE, vol. 23, pp. 14-23, 2008.
[13] P. M. Reyes, "Logistics networks: A game theory application for solving the transshipment problem," Applied Mathematics and Computation, vol. 168, pp. 1419-1431, 2005.
[14] J.-B. Sheu, "Dynamic relief-demand management for emergency logistics operations under large-scale disasters," Transportation Research Part E: Logistics and Transportation Review, vol. 46, pp. 1-17, 2010.
[15] F.-S. Chang, J.-S. Wu, C.-N. Lee, and H.-C. Shen, "Greedy-search-based multi-objective genetic algorithm for emergency logistics scheduling," Expert Systems with Applications, vol. 41, pp. 2947-2956, 2014.
[16] S. Yan and Y.-L. Shih, "Optimal scheduling of emergency roadway repair and subsequent relief distribution," Computers & Operations Research, vol. 36, pp. 2049-2065, 2009.
[17] W. Yi and L. Özdamar, "A dynamic logistics coordination model for evacuation and support in disaster response activities," European Journal of Operational Research, vol. 179, pp. 1177-1193, 2007.
[18] M. Ahmadi, A. Seifi, and B. Tootooni, "A humanitarian logistics model for disaster relief operation considering network failure and standard relief time: A case study on San Francisco district," Transportation Research Part E: Logistics and Transportation Review, vol. 75, pp. 145-163, 2015.
[19] S. Liu, W.-J. Tan, G.-D. Yu, and F. Li, "An emergency dispatch model considering the urgency of the requirement for reliefs in different disaster areas," 2015, vol. 8, p. 26, 2015-12-09 2015.
[20] X. Bai, "Two-stage multiobjective optimization for emergency supplies allocation problem under integrated uncertainty," Mathematical Problems in Engineering, vol. 2016, p. 13, 2016.
[21] L. Özdamar and M. A. Ertem, "Models, solutions and enabling technologies in humanitarian logistics," European Journal of Operational Research, vol. 244, pp. 55-65, 2015.
[22] M.-S. Chang, Y.-L. Tseng, and J.-W. Chen, "A scenario planning approach for the flood emergency logistics preparation problem under uncertainty," Transportation Research Part E: Logistics and Transportation Review, vol. 43, pp. 737-754, 2007.
[23] M. S. Tehrany, B. Pradhan, and M. N. Jebur, "Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS," Journal of Hydrology, vol. 504, pp. 69-79, 2013.
[24] M. S. Tehrany, B. Pradhan, S. Mansor, and N. Ahmad, "Flood susceptibility assessment using GIS-based support vector machine model with different kernel types," Catena, vol. 125, pp. 91-101, 2015.
[25] Z.-H. Hu, J.-B. Sheu, and L. Xiao, "Post-disaster evacuation and temporary resettlement considering panic and panic spread," Transportation Research Part B: Methodological, vol. 69, pp. 112-132, 2014.
[26] T. Kong, H. Cheng, Z. Hu, and L. Yao, "Multiobjective planning of open-loop MV distribution networks using ComGIS network analysis and MOGA," Electric Power Systems Research, vol. 79, pp. 390-398, 2009.
[27] A. Y. Chen, F. Peña-Mora, and Y. Ouyang, "A collaborative GIS framework to support equipment distribution for civil engineering disaster response operations," Automation in Construction, vol. 20, pp. 637-648, 2011.
[28] J.-G. Liu, P. J. Mason, E. Yu, M.-C. Wu, C. Tang, R. Huang, et al., "GIS modelling of earthquake damage zones using satellite remote sensing and DEM data," Geomorphology, vol. 139-140, pp. 518-535, 2012.
[29] K. Kalabokidis, N. Athanasis, F. Gagliardi, F. Karayiannis, P. Palaiologou, S. Parastatidis, et al., "Virtual Fire: A web-based GIS platform for forest fire control," Ecological Informatics, vol. 16, pp. 62-69, 2013.
[30] I.-A. Yeo and J.-J. Yee, "A proposal for a site location planning model of environmentally friendly urban energy supply plants using an environment and energy geographical information system (E-GIS) database (DB) and an artificial neural network (ANN)," Applied Energy, vol. 119, pp. 99-117, 2014.
[31] M. Safeer, S. P. Anbuudayasankar, K. Balkumar, and K. Ganesh, "Analyzing transportation and distribution in emergency humanitarian logistics," Procedia Engineering, vol. 97, pp. 2248-2258, 2014.
[32] Y.-J. Zheng, S.-Y. Chen, and H.-F. Ling, "Evolutionary optimization for disaster relief operations: A survey," Applied Soft Computing, vol. 27, pp. 553-566, 2015.
[33] L. Özdamar, E. Ekinci, and B. Küçükyazici, "Emergency logistics planning in natural disasters," Annals of Operations Research, vol. 129, pp. 217-245, 2004.
[34] T.-Y. Chou, T.-K. Liu, C.-N. Lee, and C.-R. Jeng, "Method of inequality-based multiobjective genetic algorithm for domestic daily aircraft routing," Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, vol. 38, pp. 299-308, 2008.
[35] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, "A fast and elitist multiobjective genetic algorithm: NSGA-II," Evolutionary Computation, IEEE Transactions on, vol. 6, pp. 182-197, 2002.
[36] S. N. Sivanandam and S. N. Deepa, Introduction to genetic algorithms: Springer Berlin Heidelberg, 2007.