此論文致力於解決一個組合最佳化的問題叫做Participant Selection Problem (志工指派問題)。PSP為解決如何挑選適合的志工進行勘災，以利提供災情資訊於彌補感測器無法覆蓋之地區。我們規劃四種PSP模型用來處理不同災害情境下的志工分配應對，其中我們專注於PSP-Frugal與PSP-Practical這兩個PSP模型的效能評估。我們透過MATLAB在不同機率分布下亂數產生的各個志工的貢獻值及花費值，以及根據不同變異係數用於表示災區之間的嚴重程度。此論文透過現有且強力的最佳化求解器及一個啟發式演算法PSP-G來解決PSP問題，並從中比較各個求解器及演算法的效能。我們希望透過實驗來得知在特定災害情況下選擇適合的求解器來解決PSP，以利於災害中心在指派志工上能更加有效率。

This main focus of this thesis is to solve a combinatorial optimization problem called the Participant Selection Problem (PSP). PSP aims to find an assignment of selected participants assigned to regions that their reports can eliminate blind spots and improve resolution in sensor coverage. We formulated four variants of PSP to take into account of requirements and constraints in the participant selection for some disaster scenarios. This thesis presents the performance evaluation of PSP-Frugal and PSP-Practical because they are practical assignments for real situations. We generated synthetic parameters of PSS and TAS with different probability distributions by MATLAB. PSS includes the number of volunteers, the benefit and cost values of each volunteer, and the total budget. TAS includes the number of regions and the values of the regions. We use the evaluated solvers and the PSP-G heuristic algorithm to solve the PSP, and compared relative performance by figure of merits. We hope to show some insights that which evaluated solvers and the PSP-G algorithm can produce good solutions in available time with different characteristics of problem instances.

[1] Gurobi, http://www.gurobi.com/
[2] MOSEK, http://www.mosek.com/
[3] XPRESS, http://www.fico.com/en/products/fico-xpress-optimization-suite/
[4] CPLEX, www.ibm.com/software/commerce/optimization/cplex-optimizer/
[5] Cbc (COIN-OR Branch and Cut), https://projects.coin-or.org/Cbc
[6] E. T. Chu, Yi-Lung Chen, Jyun-You Lin, and J. W. S. Liu, "Crowdsourcing support system for disaster surveillance and response," Proceedings of the 15th International Conference on Wireless Personal Multimedia Communication, pp. 21-25, Sept. 2012.
[7] O. Erhun Kundakcioglu and M. Panos Pardalos, "Generalized assignment problem," Encyclopedia of Optimization, Springer, pp. 1153-1162, 2009.
[8] Charu C. Aggarwal and Tarek Abdelzaher, "Integrating Sensors and Social Networks," Social Network Data Analytics, Springer, pp. 379-412, 2011.
[9] R. F. Ahmad, A. S. Malik, A. Qayyum and N. Kamel, "Disaster monitoring in urban and remote areas using satellite stereo images: A depth estimation approach," 2015 IEEE 11th International Colloquium on Signal Processing Its Applications, pp. 150-155, March 2015.
[10] J. W. S. Liu, E. T. H. Chu, and P. H. Tsai, "Fusing Human Sensor and Physical Sensor Data," Proceedings of International Workshop on Resilient ICT for Management of Mega Disasters, IEEE CS Press, December 2012.
[11] E. T.-H Chu, C.-Y. Lin, P. H. Tsai and J. W. S. Liu, "Design and Implementation of Participant Selection for Crowdsourcing Disaster Information," International Journal on Safety and Security Engineering, WIT Press, volume 5, number 1, pages 48-62, March 2015.
[12] E. T.-H. Chu, J. W. S. Liu and J. K. Zao, "Strategies for Crowd Sourcing for Disaster Situation Information," WIT Transactions on the Built Environment, volume 119, pages 257-269, May 2011.
[13] GAMS (General Algebraic Modeling System), http://www.gams.com/
[14] NEOS Server, http://neos.mcs.anl.gov/neos/solvers/index.html
[15] Wang Suxin, Wang Leizhen, Li Yongqing and Sun Jianyong, "Study on ant colony optimization for people assign to job problem", The 2012 24th Chinese Control and Decision Conference (CCDC), pp. 872-874, May 2012.
[16] Wang Yu and Sun Hong, "Application of Simulated Annealing Algorithm in Airline Fleet Assignment Problem", The 2010 International Conference on Intelligent System Design and Engineering Application (ISDEA), vol. 1, pp. 364-367, Oct. 2010.
[17] Lu Zaixin and W.W. Li, "Approximation algorithms for maximum target coverage in directional sensor networks", The 2014 IEEE 11th International Conference on Networking, Sensing and Control, pp. 155-160, April 2014.
[18] E. Lanzarone, A. Matta and E. Sahin, "Operations Management Applied to Home Care Services: The Problem of Assigning Human Resources to Patients", IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, pp. 1346-1363, Nov. 2012.
[19] B. Alidaee, Wang Haibo and F. Landram, "On the Flexible Demand Assignment Problems: Case of Unmanned Aerial Vehicles", The IEEE Transactions on Automation Science and Engineering, vol. 8, no. 4, pp. 865-868, Oct. 2011.
[20] Mutsunori Yagiura, Shinji Iwasaki, Toshihide Ibaraki and Fred Glover, "A very large-scale neighborhood search algorithm for the multi-resource generalized assignment problem", Discrete Optimization, vol. 1, no. 1, pp. 87-98, June 2004.
[21] R.F. Subtil, E.G. Carrano, M.J.F. Souza and R.H.C. Takahashi, "Using an enhanced integer NSGA-II for solving the multiobjective Generalized Assignment Problem", The 2010 IEEE Congress on Evolutionary Computation, pp. 1-7, July 2010.
[22] O. Dridi, S. Krichen and A. Guitouni, "A multi-objective optimization approach for resource assignment and task scheduling problem: Application to maritime domain awareness," The 2012 IEEE Congress on Evolutionary Computation (CEC), pp. 1-8, June 2012.
[23] J. Li, J. Chen, B. Xin and L. Dou, "Solving multi-objective multi-stage weapon target assignment problem via adaptive NSGA-II and adaptive MOEA/D: A comparison study," The 2015 IEEE Congress on Evolutionary Computation, pp. 3132-3139, May 2015.
[24] M. Le Berre, F. Hnaien and H. Snoussi, "Multi-objective optimization in wireless sensors networks," Proceeding of ICM, pp. 1-4, Dec. 2011.
[25] F. Glover, H. Hultz, and D. Klingman, "Improved computer based planning techniques—Part II," Interfaces, vol. 9, no. 4, pp. 12–20, 1979.
[26] P. Avella, M. Boccia and I. Vasilyev, "A Branch-and-Cut Algorithm for the Multilevel Generalized Assignment Problem", IEEE Access, pp. 475-479. 2013.
[27] B. Kiraz and H.R. Topcuoglu, "Hyper-heuristic approaches for the dynamic generalized assignment problem", The 2010 10th International Conference on Intelligent Systems Design and Applications, pp. 1487-1492, Nov. 2010.
[28] Konstantin Kogan and Avraham Shtub, "DGAP - The Dynamic Generalized Assignment Problem," Annals of Operations Research, Springer, vol. 69, pp. 227-239, 1997.
[29] Luigi Moccia, Jean-Francois Cordeau, Maria Flavia Monaco and Marcello Sammarra, "Formulations and Solution Algorithms for a Dynamic Generalized Assignment Problem", June 2007.
[30] Approximation algorithm, http://en.wikipedia.org/wiki/Approximation_algorithm
[31] Hungarian algorithm, http://en.wikipedia.org/wiki/Hungarian_algorithm
[32] D. G. Cattrysse, and L. N. V. Wassenhove, "A survey of algorithms for the generalized assignment problem," European Journal of Operational Research, vol. 60, pp. 260-272, Aug. 1992.
[33] Luo Lingzhi, N. Chakraborty and K. Sycara, "Distributed algorithm design for multi-robot generalized task assignment problem", The 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4765-4771, Nov. 2013.
[34] S. Gueye, S. Michel and A. Yassine, "A 0-1 linear programming formulation for the Berth Assignment Problem," The 2011 4th International Conference on Logistics, pp. 50-54, May 2011.
[35] R. Cohen, L. Katzir and D. Raz, "An efficient approximation for the Generalized Assignment Problem," Information Processing Letters, vol. 100, pp. 162-166, Nov. 2006.
[36] Sheng-Yuan Shen, "A stepwise incomplete branch-and-bound method for the generalized assignment problem," The 2010 40th International Conference on Computers and Industrial Engineering, pp. 1-6, July 2010.
[37] S. Haddadi and H. Ouzia, "Effective algorithm and heuristic for the generalized assignment problem," European Journal of Operational Research, vol. 153, pp. 184-190, Feb. 2004.
[38] Juan A. Dı́az and Elena Fernández, "A Tabu search heuristic for the generalized assignment problem," European Journal of Operational Research, vol. 132, pp. 22-38, July 2011.
[39] Luo Jia-xiang and Zhang Mei, "A Lagrangian based heuristic for a new class of assignment problem", IEEE International Conference on Intelligent Computing and Intelligent Systems, vo1. 2, pp. 803-807, Nov. 2009.
[40] Linear programming, https://en.wikipedia.org/wiki/Linear_programming
[41] Mixed-Integer Linear Programming Algorithms,
http://www.mathworks.com/help/optim/ug/mixed-integer-linear-programming-algorithms.html
[42] Arnold Neumaier, Oleg Shcherbina and Waltraud Huyer, "A comparison of complete global optimization solvers," Mathematical Programming, Springer, vol. 103, no. 2, pp. 335-356, 2005.
[43] BARON, https://en.wikipedia.org/wiki/BARON
[44] LINGO, http://www.lindo.com/
[45] OQNLP, https://www.gams.com/presentations/present_lasdon.pdf
[46] ICOS, https://sites.google.com/site/ylebbah/icos
[47] MINOS, http://www.sbsi-sol-optimize.com/asp/sol_products_minos_desc.htm
[48] COCONUT, http://www.mat.univie.ac.at/~neum/glopt/coconut/
[49] Gurobi Performance Benchmarks, http://www.gurobi.com/pdfs/benchmarks.pdf
[50] SCIP, http://scip.zib.de/
[51] GLPK, https://www.gnu.org/software/glpk/
[52] LPSOLVE, http://lpsolve.sourceforge.net/5.5/
[53] Benchmarks for Optimization Software, http://plato.asu.edu/bench.html
[54] NETLIB, http://www.netlib.org/
[55] MIPLIB, http://miplib.zib.de/
[56] B. Meindl and M. Templ, "Analysis of Commercial and Free and Open Source Solvers for the Cell Suppression Problem," Journal of Trans. Data Privacy, vol. 6, pp. 147-159, Aug. 2013.
[57] Matteo Fischetti and Juan José Salazar, "Modeling and Solving the Cell Suppression Problem for Linearly-Constrained Tabular Data," Proceedings in Statistical Data Protection, pp. 401-409, 1997.
[58] M. E. Ison and R. Caire, "Commercial linear programming solvers and their applications to power system optimization," The 2008 IEEE on Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy, pp. 1-7, July 2008.
[59] L. M. Hvattum, A. Løkketangen and F. Glover, "Comparisons of Commercial MIP Solvers and an Adaptive Memory (Tabu Search) Procedure for a Class of 0-1 Integer Programming Problems," Journal of Algorithmic Operations Research, vol. 7, pp. 13-21, 2012.
[60] Algebraic modeling language,
https://en.wikipedia.org/wiki/Algebraic_modeling_language
[61] AIMMS, http://aimms.com/
[62] AMPL, http://ampl.com/
[63] MATLAB, http://www.mathworks.com/products/matlab/
[64] Mixed Integer Programming Basics,
http://www.gurobi.com/resources/getting-started/mip-basics
[65] Cutting-plane method, https://en.wikipedia.org/wiki/Cutting-plane_method
[66] An Intro to LP and the Simplex Algorithm Primal Simplex,
http://www.cs.sfu.ca/CourseCentral/815/lou/LPTut/lpintro.pdf
[67] Mihai Banciu, "Dual Simplex," pp. 1-13, Jan. 2010.
[68] Interior point method, https://en.wikipedia.org/wiki/Interior_point_method
[69] GAMS Data Exchange, http://www.gams.com/mccarl/gdxusage
[70] Bin packing problem, https://en.wikipedia.org/wiki/Bin_packing_problem
[71] First-fit algorithm, http://kam.mff.cuni.cz/~sgall/ps/FFabs.pdf
[72] List of optimization software,
https://en.wikipedia.org/wiki/List_of_optimization_software