簡易檢索 / 詳目顯示

研究生: 林湘沅
Hsiang-yuan Lin
論文名稱: 應用作業研究技術於醫療保健管理問題
Applying Operations Research Techniques to Healthcare Management Problems
指導教授: 廖慶榮
Ching-jong Liao
口試委員: 陳雲岫
none
胡同來
none
李國光
none
郭人介
none
王孔政
none
學位類別: 博士
Doctor
系所名稱: 管理學院 - 管理研究所
Graduate Institute of Management
論文出版年: 2010
畢業學年度: 98
語文別: 英文
論文頁數: 80
中文關鍵詞: 排程變動鄰域搜尋法旅行業務員選址簡單加權模糊系統決策
外文關鍵詞: Scheduling, Variable neighborhood search, Travel salesman, Location selection, Fuzzy simple additive weighting system, Decision-making
相關次數: 點閱:329下載:5
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 作業觀點的醫療保健服務研究是近年來備受矚目的新興研究領域。針對醫療保健管理問題,應用作業管理工具提出嚴謹的處理方案,不僅受到學術界的討論,同時也受到醫療業的重視。因此,本論文應用作業管理技術求解醫療保健服務產業的一些實務問題。具體而言,我們發展出有效的作業研究方法,分別解決兩個業界提出的實務問題,包括嬰兒配方奶粉推廣之醫院拜訪排程問題,以及健康檢查中心開業選址問題。
    首先,我們為台灣一家國際性的嬰兒配方奶粉公司,發展一套改善的變動鄰域搜尋法,以探討其推廣業務代表的醫院拜訪排程問題。在世界衛生組織母奶哺餵保護規範下,嬰兒配方奶粉業者只能依賴醫護人員的處方或建議來推廣產品。因此,業者的地區推廣業務代表拜訪所選定的醫院之醫護人員,成為其業務推廣最重要的活動,通常這些推廣業務代表必須依循一些公司的限制規定來製備其每月的醫院拜訪排程。本研究之目的在於求解該醫院拜訪排程的最小總旅行時間,我們先發展一套四階段啟發式演算法快速地產生一個可行解,再應用已結合記憶與輪轉巧技的變動鄰域搜尋法來增進求解品質。經由廣泛的計算實驗顯示,本研究所研擬的求解方案不論在求解品質與計算時間方面均相當有效。
    其次,我們應用一套簡單加權模糊系統,探討由四個醫師組成之投資團隊所提出在大台北都會區的健康檢查中心開業選址問題。簡單加權模糊系統是一套使用主觀與客觀(質化與量化)準則的團體決策評選流程,該流程在本研究展現出其為有效的求解方案。簡單加權模糊系統整合了模糊集理論、因素評分系統與簡單加權法,常被應用於處理質化與量化的尺度評量,該評選系統的特性是在流程中可以將每位評選者的專業經驗與決策權力予以加權處理。本實務個案研究應用簡單加權模糊系統有效地評選出最佳健康檢查中心開業選址,提供投資者進一步評量與開業交涉協商。
    在未來的應用研究上,本論文所研擬的變動鄰域搜尋法可應用於求解具有相同起點與終點之週期性拜訪顧客的排程問題,例如:醫藥產品或醫療保健產品公司的醫院拜訪排程。本論文所採用的簡單加權模糊系統由於兼容人因工程的設計規則,除了設施選址外,也可用於求解其他管理決策問題,例如:專案管理、供應商評選與人才甄選等問題。


    Research on operational aspects of healthcare delivery is emerging recently. Using tools of operations management to provide a rigorous methodological treatment of a practically relevant healthcare management problem is not only discussed by academic societies but also concerned by the industry. In this dissertation, we apply operations management techniques for the real-world management problems in the healthcare industry. We attempt to develop efficient operations methods to solve the two initiated problems, including hospital call scheduling of infant formula promotion, and health examination institute location selection.
    First, we develop an improved variable neighborhood search (VNS) to investigate the hospital call scheduling of promotional sales representatives (PSRs), a real-world case provided by an international infant formula company operating in Taiwan. In the infant formula industry, products must rely on the prescription or recommendation from healthcare physicians (HCPs) under the restriction of WHO (World Health Organization) Code. Therefore, it is the most important activity in sales promotion to call on the identified HCPs in the prospected hospitals by the territory PSRs. The PSRs have to establish their monthly hospital call schedule according to several constraints. The study objective is minimizing the total travel time of the hospital call schedule. We present a four-phase heuristic to quickly generate a feasible schedule, and then apply a VNS incorporated with memory and revolver schemes to improve the solution quality. Through extensive computational experiments, it is shown that the proposed solution approaches are quite effective in terms of solution quality and computation time.
    Second, we apply a fuzzy simple additive weighting system (FSAWS) to investigate a health examination institution (HEI) location selection in Taipei Metropolitan raised by an investing team, composed of four physicians. FSAWS, an evaluation procedure by using subjective/objective (qualitative/quantitative) criteria under group decision-making conditions demonstrates that it is an effective approach. By integrating fuzzy set theory, factor rating system and simple additive weighting, FSAWS is applied to deal with qualitative and quantitative dimensions. The characteristic of this method is that the procedure considers the professional experience or authority importance of each decision maker. In the presented practical case, the final solution is obtained by identifying the optimal site of HEI for further evaluation and negotiation.
    The improved VNS approach can be applied to solve the scheduling problems which have the same starting and ending point to call on the identified customers periodically, such as in the companies of pharmaceutical or other healthcare products. The FSAWS approach can be applied to solve the facility location selection problems. Because the procedure is compliant with design rules in ergonomics, FSAWS can be applicable to other management decision problems, such as project management, supplier evaluation, and personnel selection.

    CHINESE ABSTRACT I ENGLISH ABSTRACT III ACKNOWLEDGEMENTS V CONTENTS VI LIST OF FIGURES VIII Chapter 1 INTRODUCTION 1 1.1 Motivation 1 1.2 Research objectives 3 1.3 Organization of dissertation 4 Chapter 2 LITERATURE REVIEW 7 2.1 Traveling salesman problem 7 2.2 Variable neighborhood search 7 2.3 Location selection problem 8 2.4 Fuzzy simple additive weighting system 8 Chapter 3 HOSPITAL CALL SCHEDULING 11 3.1 Problem description 11 3.2 Proposed heuristic 14 3.3 Variable neighborhood search 26 3.3.1 Neighborhood structure 26 3.3.2 VNS structure 27 3.3.3 Proposed VNS 28 3.4 Experimental Results 29 3.4.1 Selection of neighborhood structures 30 3.4.2 Comparative evaluation of the proposed VNS 33 3.5 Summary 35 Chapter 4 HEALTH EXAMINATION INSTITUTION LOCATION SELECTION 37 4.1 Problem description 37 4.2 Theoretical background 39 4.2.1 Fundamentals of fuzzy set theory 39 4.2.2. The FSAWS procedures 44 4.3 An empirical study 50 4.3.1. Illustrative example 50 4.3.2 Discussion 57 4.4 Summary 60 Chapter 5 CONCLUSIONS AND FUTURE STUDIES 62 5.1 Conclusions 62 5.2 Management implication 64 5.3 Future studies 65 REFERENCES 66 APPENDIXES 73

    REFERENCES
    [1] Pinedo, M. (2008). Scheduling: Theory, Algorithm, and System. Third Ed., Springer: New York.
    [2] Porter, M. E. (1994). “The role of location in competition,” International Journal of the Economics of Business, 1, 35–40.
    [3] Wu, C. R., Lin, C. T., & Chen, H. C. (2007). “Optimal selection of location for Taiwanese hospitals to ensure a competitive advantage by using the analytic hierarchy process and sensitivity analysis,” Building and Environment, 42, 1431–1444.
    [4] Lawler, E. L., Lenstra, J. K , Rinnooy, Kan A. H. G., & Shmoys, D. B. (1985). The Traveling Salesman Problem: A Guided Tour of Combinatorial Optimization. Wiley & Sons: Chichester.
    [5] Glover, F., & Punnen, A. P. (1997). “The travelling salesman problem: New solvable cases and linkages with the development of approximation algorithms,” Journal of the Operational Research Society, 48, 502–510.
    [6] Babin, G., Deneault, S., & Laporte, G. (2007). “Improvements to the Or-opt heuristic for the symmetric travelling salesman problem,” Journal of the Operational Research Society, 58, 402–407.
    [7] Glover, F., & Kochenberger, G. A. (2003). Handbook of Metaheuristics. Kluwer Academic Publisher: Boston.
    [8] Gen, M., & Cheng, R. (1997). Genetic Algorithms and Engineering Design. Wiley & Sons: New York.
    [9] Golden, B. L., & Skiscim, C. C. (2006). “Using simulated annealing to solve routing and location problems,” Naval Research Logistics Quarterly, 33, 261–270.
    [10] Caballero, R., González M., Guerrero, F. M., Molina, J., & Paralera C. (2007). “Solving a multiobjective location routing problem with a metaheuristic based on tabu search. Application to a real case in Andalusia,” European Journal of Operational Research, 177, 1751–1763.
    [11] Akjiratikarl, C., Yenradee, P., & Drake, P. R. (2007). “PSO-based algorithm for home care worker scheduling in the UK,” Computers & Industrial Engineering, 53, 559–583.
    [12] Naimi, H. M., & Taherinejad, N. (2009). “New robust and efficient ant colony algorithms: Using new interpretation of local updating process,” Expert Systems with Applications, 36, 481–488.
    [13] Felipe, Á., Ortuño, M. T., & Tirado, G. (2009). “The double traveling salesman problem with multiple stacks: a variable neighborhood search approach,” Computers and Operating Research, 36, 2983–2993.
    [14] Mladenović, N., & Hansen, P. (1997). “Variable neighborhood search,” Computers and Operations Research, 24, 1097–1100.
    [15] Ribeiro, C. C., & Souza, M. C. (2002). “Variable neighborhood search for the degree-constrained minimum spanning tree problem,” Discrete Applied Mathematics, 118, 43–54.
    [16] Avanthay, C., Hertz, A., & Zufferey, N. (2003). “A variable neighborhood search for graph coloring,” European Journal of Operational Research, 151, 379–388.
    [17] Kytöjoki, J., Nuortio, T., Bräysy, O., & Gendreau, M. (2007). “An efficient variable neighborhood search heuristic for very large scale vehicle routing problems,” Computers and Operations Research, 34, 2743–2757.
    [18] Liao, C. J., & Cheng, C. C. (2007). “A variable neighborhood search for minimizing single machine weighted earliness and tardiness with common due date,” Computers and Industrial Engineering, 52, 404–413.
    [19] Wang, X., & Tang, L. (2009). “A population-based variable neighborhood search for the single machine total weighted tardiness problem,” Computers and Operations Research, 36, 2105–2110.
    [20] Roshanaei, V., Naderi, B., Jolai, F., & Khalili, M. (2009). “A variable neighborhood search for job shop scheduling with set-up times to minimize makespan,” Future Generation Computer System, 25, 654–661.
    [21] Hansen, P., Mladenović, N., & Pérez, J. A. M. (2009). “Variable neighbourhood search: methods and applications,” Annals of Operations Research, 175, 367–407.
    [22] Salminen, P., Hokkanen, J., & Lahdelma, R. (1998). “Comparing multicriteria methods in the context of environmental problems,” European Journal of Operational Research, 104, 485–496.
    [23] Cheng, S., Chan, C. W., & Huang, G. H. (2002). “Using multiple criteria decision analysis for supporting decisions of solid waste management,” Journal of Environmental Science & Health. A37, 975–990.
    [24] Marinoni, O. (2005). “A stochastic spatial decision support system based on PROMETHEE,” International Journal of Geographical Information Science, 19, 51–68.
    [25] Lin, C. T., Wu, C. R., & Chen, H. C. (2008). “The study of construct key success factors for the Taiwanese hospitals of location selection by using the fuzzy AHP and sensitivity analysis,” Information and Management Sciences, 19, 175–200.
    [26] Wu, C. R., Lin, C. T., & Chen, H. C. (2009). “Integrated environmental assessment of the location selection with fuzzy analytical network process,” Qual Quant, 43, 351–380.
    [27] Lin, C. T., & Tsai, M. C. (2009). “Development of an expert selection system to choose ideal cities for medical service ventures,” Expert Systems with Applications, 36, 2266–2274.
    [28] Lin, C. T., & Tsai, M. C. (2010). “Location choice for direct foreign investment in new hospitals in China by using ANP and TOPSIS,” Qual Quant, 44, 375–390.
    [29] Vahidnia, M. H., Alesheikh, A. A., & Alimohammadi, A. (2009). “Hospital site selection using fuzzy AHP and its derivatives,” Journal of Environmental Management, 90, 3048–3056.
    [30] Chou, S. Y., Chang, Y. H., & Shen, C. Y. (2008). “A fuzzy simple additive weighting system under group decision-making for facility location selection with objective/subject attributes,” European Journal of Operational Research, 189, 132–145.
    [31] Ou, C. W., & Chou, S. Y. (2009). “International distribution center selection from a foreign market perspective using a weighted fuzzy factor rating system,” Expert Systems with Applications, 36, 1773–1782.
    [32] Finch, B. J. (2007). Operation Now: Profitability, Process, Performance. Second Ed., McGraw-Hill: New York.
    [33] Heizer, J., & Render, B. (2010). Principles of Operational Management. Eighth Ed., Printice Hall: New Jersey.
    [34] Stevenson, W. J. (2008). Operations Management. Tenth Ed., McGraw-Hill: New York.
    [35] Porter, M. E. (1990). The Competitive Advantage of Nations. Free Press: New York.
    [36] De Paula, M. R., Ravetti, M. G., Mateus, G. R., & Pardalos, P. M. (2007). “Solving parallel machines scheduling problems with sequence-dependent setup times using variable neighbourhood search,” IMA Journal of Management Mathematics, 18, 101–115.
    [37] Coello, C. A. C. (1999). A Survey of Constraint Handling Techniques Used with Evolutionary Algorithms. Laboratorio Nacional deInformtica Avanzada: Veracruz, Mexico.
    [38] Blum, C., & Roli, A. (2003). “Metaheuristics in combinatorial optimization: Overview and conceptual comparison,” ACM Computing Surveys, 35, 268–308.
    [39] Hansen, P., & Mladenović, N. (2001). “Variable neighborhood search: Principles and applications,” European Journal of Operational Research, 130, 449–467.
    [40] Logendran, R., McDonell, B., & Smucker, B. (2007). “Scheduling unrelated parallel machines with sequence-dependent setups,” Computers & Operations Research, 34, 3420–3438.
    [41] Zadeh, L. A. (1965). “Fuzzy sets,” Information and Control, 8, 338–353.
    [42] Keufmann, A., & Gupta, M. M. (1991). Introduction of Fuzzy Arithmetic: Theory and Application. Van Nostrand Reinhold: New York.
    [43] Dubois, D., & Prade, H. (1978). “Operations on fuzzy numbers,” International Journal of Systems Science, 9, 613–626.
    [44] Chiou, H. K., Tzen, G. H., & Cheng, D. C. (2005). “Evaluating sustainable fishing development strategies using fuzzy MCDM approach,” Omega, 33, 223–234.
    [45] Chen, S. J., & Hwang, C. L. (1992). Fuzzy Multiple Attributes Decision Making: Methods and Applications. Springer-Verlag: New York.
    [46] Zhao, R., & Govind, R. (1991). “Algebraic characteristics of extended fuzzy numbers,” Information Science, 54, 103–130.
    [47] Yager, R. R., & Filev, D. P. (1994). Essentials of Fuzzy Modeling and Control. Wiley: New York.
    [48] Tsaur, S. H., Tzen, G. H., & Wang, G. C. (1997). “Evaluating tourist risks from fuzzy perspectives,” Annals of Tourism Research, 24, 796–812.
    [49] Tang, M. T., Tzen, G. H., & Wang, S. W. (1999). “A hierarchy fuzzy MCDM method for studying electronic marketing strategies in the information service industry,” Journal of International Information Management, 8, 1–22.
    [50] Yao, J. S., & Wu K. (2000). “Ranking fuzzy numbers based on decomposition principle and signed distance,” Fuzzy Sets and Systems, 116, 275–288.
    [51] Klic, G. I., & Yan, B. (1995). Fuzzy Sets and Fuzzy Logic Theory and Applications. Prentice-Hall: London.
    [52] Yao, J. S., & Chiang, J. (2003). “Inventory without backorder with fuzzy total cost and fuzzy storing cost defuzzified by centroid and signed distance,” European Journal of Operational Research, 148, 401–409.
    [53] Yetton, P., & Botter, P. (1983). “The relationships among group size, member ability, social decision schemes, and performance,” Organizational Behavior and Human Performance, 32, 145–159.
    [54] Liang, G. S., & Wang, M. J. J. (1991). “A fuzzy multi-criteria decision-making method for facility site selection,” International Journal of Production Research, 29, 2313–2330.
    [55] Liang, G. S. (1999). “Fuzzy MCDM based on ideal and anti-ideal concepts” European Journal of Operational Research, 112, 682–691.
    [56] Glover, F. (1986). “Future paths for integer programming and links to artificial intelligence.” Computers & Operations Research, 13, 533-549.

    QR CODE