在這講求高效率的年代，不論所處的環境為何，都一直不斷的強調如何在有限資源下取得最大利益。為了讓資源分配達到最佳效益，本篇論文提出以計算式智慧的方法來解決兩個資源分配的問題，一個是空戰戰機接戰決策的問題，另一為手術室排程規劃的問題。
在數位化戰場時代，透過科技提供迅速且有效的協助，將成為未來致勝關鍵。因此本論文將研究空戰戰場上戰機接戰決策的應用，提供符合最佳優勢的接戰決策策略。在空戰戰機接戰決策問題中，透過取得的空戰態勢，利用多層感知機來分析戰機遭遇時間，並評估戰機接戰時的優勢與設計反制策略的評估函式。透過自我組織特徵映射圖最佳化演算法提供最佳的反制策略，輔助決策者分析與選擇，以提昇戰機接戰決策之效能。
醫院投注許多資金於手術醫療設備上，手術室對一間醫院營運而言，扮演著舉足輕重的角色，好的排程方式會讓手術室的使用效率變高，進而提昇醫院的營運效率。本論文針對手術室排程規劃，提出基因演算法與自我組織特徵映射圖最佳化演算法兩個最佳化演算法，以便從眾多手術室排程的方法中，找出一個讓使用效率達到最佳的排程的方法，增進手術室的使用效率。

In this efficient-oriented generation, making much of pursuing the best benefit with restrictive resources is very important. How to optimize an objective function under precedence restrictions is a very demanding and challenging problem. This study proposes a computational-intelligence-based approach for solving two kinds of resource allocation problems. The first kind resource allocation problem is the aircraft assignment tactic problem and the other one is the operating room scheduling problem.
On modem air warfare, it is important for a commander to quickly and effectively make a proper assignment of our aircrafts to attack enemy aircrafts. The goal of the aircraft assignment tactic problem is to find a proper tactic to assign our aircrafts to meet head-on enemy aircrafts with the objective of maximizing the expected predominance. One important factor in the computation of the expected preponderance value is the prediction of the flying time needed by our aircraft to encounter an enemy aircraft. We train an artificial neural network to predict the flying time based on 11 important features (e.g., the velocities, directions, distance, etc). After the preponderance matrix has been generated, the SOM-based optimization (SOMO) algorithm is adopted to find an assignment which can maximize the expected preponderance value.
Operating rooms play a decisive role on a hospital’s finance because there are usually a lot of expensive instruments in the rooms and a lot of resources are also needed there. Therefore, effective operating room scheduling can reduce costs and ensure efficient use of operating rooms while maintaining a good quality of care. The second part of the thesis is to develop an effective operating rooms scheduling algorithm. This study proposes a genetic-algorithm-based and a SOMO-based operating rooms scheduling algorithms to determine operation schedules which can fully improve the utility rate of operating rooms.

[1] M. V. Calichman, “Creating an Optimal Operating Room,” AORN Journal, vol. 81, no. 3, March 2005.
[2] J. Carlier and E. Pinson, “An Algorithm for Job-Shop Problem,” Management Science, vol. 35, no.2, pp.164-176, Feb. 1989.
[3] H. Cordesman, “The Instant Lessons of the Iraq War,” Center for Strategic and International Studies (CSIS), Washington, DC, Main Report, April 14, 2003, p.21.
[4] D.H. Cropley, "Information and C4ISR Systems", in Proceedings of the Systems Engineering Pragmatic Solutions to Today’s Real World Problems Conference (SE’98), Canberra, Australia, Nov. 4-6, 1998, pp.139-143.
[5] B. Denton, J. Viapiano, and A. Vogl, “Optimization of Surgery Sequencing and Scheduling Decisions under Uncertainty,” Health Care Manage Science, vol. 10, pp.13-24, 2007.
[6] R. H. Epstein and F. Dexter, “Economic Analysis of Linking Operating Room Scheduling and Hospital Material Management Information Systems for Just-in-Time Inventory Control,” Economic and Health Systems Research, vol. 91, pp.337-343, 2000.
[7] J. Gao, M. Gen, and L. Sun, “A Hybrid of Genetic Algorithm and Bottleneck Shifting for Flexible Job Shop Scheduling Problem,” Computers & Industrial Engineering, vol. 45, no. 4, pp.597-613, Dec. 2003.
[8] M. Gen and R. Cheng, Genetic algorithms and engineering design. New York: Wiley, 1997.
[9] A. Guinet and S. Chaabane, “Operating Theatre Planning,” International Journal of Production Economics, vol. 85, pp.69-81, 2003.
[10] K. H. Hanson, “Computer-Assisted Operating Room Scheduling”, International Journal of Production Economics, vol. 99, pp.52-62, 2006.
[11] T. Ibaraki and N. Katoh, Resource Allocation Problems: Algorithmic Approaches. The MIT Press, 1988.
[12] A. Jebaili, A. B. H. Alouane, and P. Ladet, “Operating rooms scheduling”, International Journal of Production Economics, vol. 99, pp.52-62, 2006.
[13] J. Kennedy, R.C. Eberhart, and Y. Shi, Swarm Intelligence. San Francisco, USA: Morgan Kaufmann Publishers, 2001.
[14] P. J. Kuzdrall, N. K. Kwak, and H. H. Schmitz, “Monte Carlo Simulation of Operating-Room and Recovery-Room Usage,” Operations Research, vol. 22, no. 2, pp.434-440, 1974.
[15] M. Lamiri, J. Dreo, and X. Xie, “Operating Room Planning with Random Surgery Times,” in Proceedings of 3rd Annual IEEE Conference on Automation Science and Engineering Scottsdale, AZ, USA, Sep. 22-25, 2007, pp.521-526.
[16] Z. J. Lee, S. F. Su, and C. Y. Lee, “Efficiently Solving General Weapon-Target Assignment Problem by Genetic Algorithms With Greedy Eugenics”, IEEE Transactions on Systems, Man, and Cybernetics—part B: Cybernetics, vol. 33, no. 1, pp.113-121, Feb. 2003.
[17] K. M. Lee, T. Yamakawa, and K.-M. Lee, “A genetic algorithm for general machine scheduling problems,” in Proceedings of 1998 Second International Conference on Knowledge-Based Intelligent Electronic Systems, Adelaide, Australia, 1998, vol. 2, pp.60-66.
[18] H. Q. Lu, H. J. Zhang, X. J. Zhang, and R. X. Han, “An Improved Genetic Algorithm for Target Assignment Optimization of Naval Fleet Air Defense”, in Proceedings of the 6th World Congress on Intelligent Control and Automation, Dalian, China, June 21-23, 2006.
[19] J. Nakasuwan, P. Srithip, and S. Komolavahij, “Class Scheduling Optimization,” Thammasat International Journal Science Tech., vol. 4, no. 2, pp.88-98, July 1999.
[20] E. Nowicki and C. Smitnicki, “A Fast Taboo Search Algorithm for the Job Shop Problem,” Management Science, vol. 42, no. 6, pp.797-813, June 1996.
[21] L. F. Perrone, F. P. Wieland, J. Liu, B. G. Lawson, D. M. Nical, and R.M. Fujimoto, “A Bee Colony Optimization Algorithm to Job Shop Scheduling,” in Proceedings of the 2006 Winter Simulation Conference, Dec. 3-6, 2006, pp.1954-1961.
[22] M. C. Su, T. K. Liu, and H. C. Chang “Improving the self-organizing feature map algorithm using an efficient initialization scheme,” Tamkang Journal of Science and Engineering, vol. 5, no. 1, pp.35-48, March 2002.
[23] M.C. Su, Y. X. Zhao, and J. Lee, “SOM-based Optimization,” in 2004 IEEE International Joint Conference on Neural Networks, Budapest, Hungary, July 25-29, 2004, pp.781-786.
[24] W. Xia and Z. Wu, “An Effective Hybrid Optimization Approach for Multi-Objective Flexible Job-Shop Scheduling Problem,” Computer and Industrial Engineering, vol. 48, pp.409-425, 2005.
[25] Q. F. Xie, J. F. Feng, and Y. B. Zhong, “Decision of Cooperative Airfight and its Realization by Neural Network”, in Proceedings of 2006 6th International Conference on ITS Telecommunications, Chengdu, June 2006, pp.298-301.
[26] N. Zribi, I. Kacem, A. E. Kamel, and P. Born, “Assignment and Scheduling in Flexible Job-Shops by Hierarchical Optimization,” IEEE Transactions on Systems, Man, and Cybernetics-part C: Applications and Reviews, vol. 37, no. 4, pp.652-661, July 2007.
[27] 林素鉁、陳重光、侯東旭，「應用人工智慧演算法於手術室開刀作業排程規劃改善之研究」，醫療資訊雜誌，第16卷，第3期，頁35-48，2005年4月。
[28] 孫宗瀛、李延年、蔡尚錚、陳宏君、周治平，「智慧型CGF模糊決策系統之研究」，中科院94年度學術合作計畫成果發表會，2005年11月24日。
[29] 陳昭宇，「根基於自我組織特徵映射圖為基礎之最佳化演算法之推薦系統」，碩士論文，資訊工程研究所，國立中央大學，2005年7月。
[30] 陳德芳，「建立手術時間預測模式來從事電腦化手術室排程」，碩士論文，醫療機構管理研究所，國立臺灣大學，2006年7月。
[31] 程紅斌、張鳳鳴、張曉豐，「多機協同空戰目標分配算法」，空軍工程大學學報(自然科學版)，第6卷，第2期，2005年4月。
[32] 趙于翔，「以群體智慧為基礎的最佳化演算法及其應用」，博士論文，資訊工程研究所，國立中央大學，2007年7月。
[33] 蔣安仁，「開刀房利用最佳化之研究」，碩士論文，醫務管理研究所，國立中山大學，2004年6月。
[34] 蘇木春、張孝德，機器學習：類神經網路、模糊系統以及基因演算法則。全華科技圖書股份有限公司，2004年。