(3.238.36.32) 您好!臺灣時間:2021/02/27 08:28
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

我願授權國圖
: 
twitterline
研究生:康秀芳
研究生(外文):Shiu-Fang Kang
論文名稱:資料包絡分析法機率形式之效率評估
論文名稱(外文):Using Probability Form to Evaluate Efficiency in DEA
指導教授:魏乃捷魏乃捷引用關係
指導教授(外文):Nai-Chieh Wei
學位類別:博士
校院名稱:義守大學
系所名稱:工業管理學系
學門:商業及管理學門
學類:其他商業及管理學類
論文種類:學術論文
論文出版年:2016
畢業學年度:105
語文別:英文
論文頁數:78
中文關鍵詞:資料包絡分析法交叉效率區間效率機率形式之效率
外文關鍵詞:Data Envelopment Analysis (DEA)CCRCross EfficiencyInterval EfficiencyProbability Efficiency
相關次數:
  • 被引用被引用:0
  • 點閱點閱:73
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
以資料包絡分析(Data Envelopment Analysis, DEA)評估各受評單位之效率值,雖具有非常良好之功效,卻也常出現以下二種缺失:一者由於各受評單位之投入、產出變項值難以準確地獲得,因此當數據稍有變動時,則會影響效率值評估;二者假設受評單位A之效率優於受評單位B之效率,以傳統之效率評估,僅能給予第一層級之效率評比,受評單位A之效率優於受評單位B之效率。但在實際的情況下,受評單位A可能有某些部份會等於受評單位B之效率,甚至受評單位A也會有一些部份劣於受評單位B之效率。因此,改善上述二種傳統效率評估之缺失,即為本研究之主要目的。
針對上述缺失,本研究以交叉效率模式 之「強勢權重之效率評估」作為「效率值之上限」; 之「弱勢權重之效率評估」作為「效率值之下限」,以此求得該 之「區間效率」。本研究定義:假設 與 之區間效率,對於 小於 之機率,以 小於 表之;而,對於 大於 之機率,則以 大於 表之。由於〝小於〞與〝大於〞並非完全互斥,因此 小於 大於 ;為確定此定義之「完善性」,本研究以「資料包絡分析法之區間效率,進行各種機率形式效率評估,總和為〝1〞」之定理,證明此「機率形式之效率評估」完善性。如此的機率形式之效率評比,更可以解釋說明各受評單位之優劣,除了可用第一層級的效率評估,更可再做第二層級之效率評估。
將本研究之創新模式,應用於寺院服務品質績效之評估,針對20間寺院之服務品質(此時,投入變量均視為相等時,所求出的效率即為績效),發現以第一層級的CCR效率評估,績效為〝1〞的寺院有第16、第18及第19等三間;但經本研究「機率形式之區間效率評估」,由於第19間寺院之區間效率範圍〝最小〞,可認為其服務品質績效最佳,其次第18間寺院服務品質績效為第二名,第16間寺院服務品質績效為第三名。再針對「區間效率上限小於〝1〞者」,選取第12、第13、第14及第15等四間寺院,進行本研究四種機率形式之績效評估,結果發現,第15間寺院服務品質績效是優於第12間寺院的服務品質績效;第13間寺院服務品質績效是優於第12間寺院的服務品質績效;第12間寺院服務品質績效優於第14間;第15間寺院服務品質績效優於第13間寺院服務品質績效。
The Charnes, Cooper, and Rhodes (CCR) model in Data Envelopment Analysis (DEA) is highly effective at measuring the efficiency of decision-making units (DMUs). However, it has two main limitations. One is that because accurately estimating the inputs and outputs of DMUs are difficult, slight changes in these data can affect the effectiveness of the model in estimating DMU efficiency. The other is that if DMU A is more efficient than DMU B, then only elementary efficiency ratings are obtained through the CCR; in reality, DMU A is as efficient as DMU B in certain aspects but less efficient in others. This study addressed both limitations.
To overcome these two limitations, this paper proposed DEA-PE model in which the strong weight and weak weight of the cross-efficiency, , were defined as the upper and lower limits of efficiency, respectively, to estimate the interval efficiency of the DMUs. In the interval efficiencies of and , was the probability that was lower than and was the probability that was larger than . “ < ” and “ > ” are not mutually repulsive of each other; thus, . One of the principles of DEA, where all probability interval will eventually sum up to 1 is utilized in this study, in order to establish the integrity of probability efficiency evaluation. Accordingly, this probability efficiency evaluation provided a more thorough explanation of the strengths and weaknesses of two DMUs and enabled the investigation of the respective efficiency evaluations of the DMUs at the in-depth meaning.
In the study, the innovation model which described earlier is applied to the assessment of the quality of service performance of targeted 20 monasteries. All input variables are treated equal, and the performances of monasteris will be treated as their effiency respectively. First, in the first level CCR evaluation of the study, there are 3 temples credited “1” for their service qualities. They are numbered as 16th, 18th and 19th. However, by the “efficiency interval evaluation in probability form” provided in the study, temple 19th finally took the first place since it has the lowest efficiency interval, thus 18th and 16th took second and third respectively.Second, the study focus on those who are credit less than 1. Four temples, the 12th, 13th, 14th, and 15th are selected for efficiency evaluation in 4 probability distributions. The results turn out to be the monastery porfermance of temple 15 th is greater than temple 12 th; temple 13 th is also greater than temple 12 th; and the temple 12 th is greated than temple 14 th and the temple 15 th is greater than temple 13 th. Knowing is greater than, indicating that when the temples obtained by the efficiency of the range is often not necessarily the absolute strength of the weak, the temples of the variable content although some strong, but some variables variable content is weak, through this method more objective understanding of each other Which is the second level of efficiency evaluation of the characteristics of this study.
ABSTRACT (CHINESE).……………………........................…………………I
ABSTRACT(ENGLISH)……………..........................………………………II
TABLE OF CONTENTS……………..........................………………………IV
LIST OF TBLES………………………………...........................……………VI
LIST OF FGURES…………………………..........................………………VII
CHAPTER 1 INTRODUCTION……......................……….………..………1
1.1 Research Background and Motivation ..…..…....……………………3
1.2 Research Objectives…...…..……………..........……..……………………6
1.3 Research Framework……..…………...............………………………………7
CHAPTER 2 LITERATURE REVIEW…….....................….………………9
2.1 Imprecise Source Data in Data Envelopment Analysis….…9
2.2 Between-Decision-Making Unit Efficiency Evaluation.…13
2.3 Relationship Between DEA and Service Quality Performance….............................................17
CHAPTER3 RESEARCH METHODS….….....................…….……………23
3.1 Evaluating Interval Efficiencies in DEA...…..….…....23
3.2 Evaluating the Probability Distribution-based
Efficiencies of Decision Making Unit Pairs……........27
3.3 Examples……………….………………………………….…................….....31
CHAPTER 4 APPLICATION OF PROPOSED INNOVATIVE MODEL TO EVALUATE MONASTERY SERVICE QUALITY PERFORMANCE................................……....……………….…40
4.1 Monastery Service Quality Performance………………………......…40
4.2 Probability-Distribution-Based Efficiency Evaluation of Monastery Service Quality Performance………………………....………....44
CHAPTER5 CONCLUSION...............................................54
REFERENCES..……………………..……………………..........................…55
APPENDICES
APPENDIX A………………………………………..........................………...70
APPENDIX B……………………………………………..........................…...82
Akbari, P., & Darabi, A. (2015). Factors affecting customer satisfaction, quality banking services to Iran, using the SERVQUAL model case study: Resalat bank of Kermanshah province. Advanced Social Humanities and Management, 1(4), 2014.
Alavi, M., & Majidi, A. (2015). Identifying and Prioritizing Effective Factors on Assessing Service Quality of E-Government. Cumhuriyet Science Journal, 36(3), 2526-2534.
Ali, M., & Raza, S. A. (2015). Service quality perception and customer satisfaction in Islamic banks of Pakistan: the modified SERVQUAL model. Total Quality Management & Business Excellence, 1-19.
Anderson, P., & Peterson, N.C. (1993). A Procedure for Ranking Efficient Units in Data Envelopment Analysis. Management Sciences, 39(10), 1261–1264.
Angulo-Meza, L., & Lins, M. P. E. (2002). Review of methods for increasing discrimination in data envelopment analysis. Annals of Operations Research, 116(1-4), 225-242.
Azadi, M., & Saen, R. F. (2012). Developing a worst practice DEA model for selecting suppliers in the presence of imprecise data and dual-role factor. International Journal of Applied Decision Sciences, 5(3), 272-291.
Azadi, M., Saen, R. F., & Momeni, E. (2013). Developing a new neutral DEA model for media selection in the existence of imprecise data. International Journal of Operational Research, 18(1), 16-34.
Azizi, H., Kordrostami, S., & Amirteimoori, A. (2015). Slacks-based measures of efficiency in imprecise data envelopment analysis: An approach based on data envelopment analysis with double frontiers. Computers & Industrial Engineering, 79, 42-51.
Bao, C. P., Jeng, C. H., Guey, C. C., & Lin, C. L. (2014). The liner programming approach on A-P supper-efficiency data envelopment analysis model of infeasibility of solving model. American Journal of Applied Sciences, 11(4), 601.
Bao, C. P., Wei, N. C., & Kang, S. F. (2015). A DEA Model Incorporating The Overall Integral And Self-Evaluation Common Weight. International Journal of Organizational Innovation (Online), 8(2), 118.
Bao, C. P., Wei, N. C., & Kang, S. F. (2016). Supper sufficiency model using a constant as “total weighted sum of input”constraint. International Journal of organization innovation, 19(1), 146-157.
Bao, C. T. (2014). Evaluating Upper- and Lower- Limits using Data Envelop Analysis Having Assurance Regions and Emphasizing Both Expert-Assigned Weights and Survey data.
Bao, C. T. & Tsai, M. I. (2012), Designing Simple Calculation Methods of Cross Efficiency in Data Envelop Analysis. Conference paper, International Conference on Regional Development, Economic Construction, and Personnel Evaluation, Garden Villa, Kaohsiung.
Bao, C. T. & Tsai, M. I. (2016),Investigating Innovative Applications of Data Envelop Analysis. Doctoral dissertation, Department of Industrial Management, I-Shou University, 1–54。
Bao, C. T., Tsai, M. I., & Tsai, M. C. (2011). To Study the Common Compromise Weights of Data Envelopment Analysis. Management Research, 89–110.
Basfirinci, C., & Mitra, A. (2015). A cross cultural investigation of airlines service quality through integration of Servqual and the Kano model. Journal of Air Transport Management, 42, 239-248.
Beiranvand, J. M., & Khodabakhshi, M. (2013). Efficiency assessment of a banking system with imprecise data (fuzzy) with a fuzzy mathematical programming approach in DEA. Life Science Journal, 10(5s).
Chambers, R. G., & Färe, R. (2004). Additive decomposition of profit efficiency. Economics Letters, 84(3), 329-334.
Chang, S. Y. (2016), “Aging in Place” Service Mode—Combining Religious Care Case, doctoral dissertation, College of Commerce at National Chengchi University.
Chang, T. C., & Lin, S. J. (1999). Grey relation analysis of carbon dioxide emissions from industrial production and energy uses in Taiwan. Journal of Environmental Management, 56(4), 247-257.
Chang, Y. C. (2015), Social Services of Local Congregations, Journal of Community Work and Community Studies, 5(1), 35–83.
Change, C. Y., Wang, C. C., & Lai, H. S. (2014). A Study of Relationships among Motivation, Satisfaction and Revisit Intention in Religious Tourism: The Lukang-Mazu Temple. Journal of Tourism and Health Science, 13(1), 1–16.
Charles, V., & Kumar, M. (2014). Satisficing data envelopment analysis: An application to SERVQUAL efficiency. Measurement, 51, 71-80.
Charnes, A., Cooper, W. W., Lewin, A. Y., Morey, R. C., & Rousseau, J. (1984). Sensitivity and stability analysis in DEA. Annals of Operations Research, 2(1), 139-156.
Charnes, A., Cooper, W. W., Lewin, A. Y., & Seiford, L. M. (2013). Data envelopment analysis: Theory, methodology, and applications. Springer Science & Business Media.
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the Efficiency of Decision Making Units, European Journal of Operational Research, 2(6), 429~444.
Charnes, A., Cooper, W. W., Wei, Q. L., & Huang, Z. M. (1989). Cone ratio data envelopment analysis and multi-objective programming. International Journal of Systems Science, 20(7), 1099-1118.
Chen, M. B. (1995)Service Quality Management, Quality Control Journal.
Chen, Y. (2005). Measuring super-efficiency in DEA in the presence of infeasibility. European Journal of Operational Research, 161(2), 545-551.
Chen, Y. C., Chiu, Y. H., Huang, C. W., & Tu, C. H. (2013). The analysis of bank business performance and market risk—Applying Fuzzy DEA. Economic Modelling, 32, 225-232.
Cheng, G., Qian, Z., & Zervopoulos, P.(2011).Overcoming the infeasibility of super-efficiency DEA model: a model with generalized orientation.
Cook, W. D., Liang, L., Zha, Y., & Zhu, J. (2009). A modified super-efficiency DEA model for infeasibility. Journal of the Operational Research Society, 60(2), 276-281.
Cook, W. D., Roll, Y., & Kazakov, A. (1990). A Dea Model for Measuring The Relative Eeficiency Of Highway Maintenance Patrols. INFOR: Information Systems and Operational Research, 28(2), 113-124.
Cooper, W. W., Deng, H., Gu, B., Li, S., & Thrall, R. M. (2001). Using DEA to improve the management of congestion in Chinese industries (1981–1997). Socio-Economic Planning Sciences, 35(4), 227-242.
Cronin Jr, J. J., & Taylor, S. A. (1992). Measuring service quality: a reexamination and extension. The journal of marketing, 55-68.
Dabestani, R., Shahin, A., Saljoughian, M., & Shirouyehzad, H. (2016). Importance performance analysis of service quality dimensions for the customer groups segmented by DEA: The case of four star hotels. Internatio nal Journal of Quality & Reliability Management, 33(2), 160-177.                                                                                                      
Dharmapala, P. S. (2014). Randomizing Efficiency Scores in DEA Using Beta Distribution: An Alternative View of Stochastic DEA and Fuzzy DEA. International Journal of Business Analytics (IJBAN), 1(4), 1-15.
Dotoli, M., Epicoco, N., Falagario, M., & Sciancalepore, F. (2015). A cross-efficiency fuzzy data envelopment analysis technique for performance evaluation of decision making units under uncertainty. Computers & Industrial Engineering, 79, 103-114.
Doyle, J. & Green, R. (1994). Efficiency and cross-efficiency in DEA: derivations, meanings and uses. Journal of the Operational Research Society, 45, 567-578.
Doyle, J. R. (1995). Multiattribute Choice for the Lazy Decision Maker: Let the Alternatives Decide. Organizational Behavior and Human Decision Processes, 62(1), 87-100.
Dyson, R. G., & Thanassoulis, E. (1988). Reducing weight flexibility in data envelopment analysis. Journal of the Operational Research Society, 39(6), 563-576.
Ebadi, S. (2012). Using a Super Efficiency Model for Ranking Units in DEA. Applied Mathematical Sciences, 6(41), 2043-2048.
Edmister, R. O. (1972). An empirical test of financial ratio analysis for small business failure prediction. Journal of Financial and Quantitative analysis, 7(02), 1477-1493.
Eshlaghi, A. T., & Jamalou, F. (2011). A Hybrid Mathematical Model for Product Positioning Based on DEA (Case study: Automobile Dealership in UAE-Dubai). Applied Mathematical Sciences, 5(61), 3011-3020.
Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society. Series A (General), 120(3), 253-290.
Ganley, J. A. & Gubbin, J. S. (1992). Public sector efficiency measurement: applications of data envelopment analysis. Amsterdam: North-Holland.
Galeeva, R. B. (2016). SERVQUAL application and adaptation for educational service quality assessments in Russian higher education. Quality Assurance in Education, 24(3).
George, S. A. (2016). Productive efficiency, service quality and profitability: a comparative analysis of foreign and private banks in India. International Journal of Productivity and Quality Management, 18(4), 518-536.
Golany, B. (1988). An interactive MOLP procedure for the extension of DEA to effectiveness analysis. Journal of the Operational Research Society, 39(8), 725-734.
Green, R. H., Doyle, J. R., & Cook, W. D. (1996). Preference voting and project ranking using DEA and cross-evaluation. European Journal of Operational Research, 90(3), 461-472.
Hadad, Y., Friedman, L., Sinuany- Stern, Z. & Ben-Yuir, A. (2008). Ranking Method based on the difference between weighted output and input. Computer Modeling and New Technologies, 12(3): 55-65.
Haksever, C., Cook, R. G., & Chaganti, R. (2015). Applicability of the gaps model to service quality in small firms. Journal of Small Business Strategy, 8(1), 49-66.
Hossain, M. K., Kamil, A. A., Mustafa, A., & Baten, M. A. (2014). Estimating DEA Efficiency Using Uniform Distribution. Bulletin of the Malaysian Mathematical Sciences Society, 37(4).
Hosseinzadeh-Lotfi, F., Jahanshahloo, G. R., & Mohammadpour, M. (2013). An extension of cross redundancy of interval scale outputs and inputs in DEA. Journal of Applied Mathematics, 2013.
Hu, R., & Shieh, C. J. (2014). Evaluating the Performance of Service Quality in the Hospitals of Traditional Chinese Medicine with Data Envelopment Analysis. STUDIES ON ETHNO-MEDICINE, 8(1), 69-75.
Huang, X. N. (1993), On the Methods and Applications of Data Envelopment Analysis for Measuring the Efficiency of Non-profit Organizations, doctoral dissertation, Institute of Business and Management, National Chiao Tung University, 1–83.
Huang, Y. C. & Li, H. L. (2002), Modification of Discrimination of DEA Methods: Cases of Top Business Schools, master’s thesis, Institute of Business and Management, National Chiao Tung University.
Kano, N., Seraku, N., Takahashi, F., & Tsuji, S. (1984). Attractive quality and must-be quality.
Kao C., Hung H.T.(2005). Data envelopment analysis with common weights: the compromise solution approach, Journal of the Operational Research Society, 56, 1196-1203.
Karadayi, M. A., & Karsak, E. E. (2014). Imprecise DEA Framework for Evaluating the Efficiency of State Hospitals in Istanbul. In Proceedings of the World Congress on Engineering, 2.
Karsak, E. E., & Dursun, M.(2013). An Integrated QFD-DEA Framework with Imprecise Data for Supplier Selection. In Proc of the World Congress on Engineering, 1.
Karsak, E. E., & Dursun, M. (2014). An integrated supplier selection methodology incorporating QFD and DEA with imprecise data. Expert Systems with Applications, 41(16), 6995-7004.
Khodabakhshi, M. (2007). A super-efficiency model based on improved outputs in data envelopment analysis. Applied Mathematics and Computation, 184(2), 695-703.
Khodabakhshi, M., & Aryavash, K. (2014). Ranking units with fuzzy data in DEA. Data Envelopment Analysis and Decision Science, 2014(2014), 1-10.
Khodabakhshi, M., & Rashnoo, K. (2013). An assurance interval for non-Archimedean ε in imprecise data envelopment analysis (IDEA). Data Envelopment Analysis and Decision Science, 2013.
Kuo, P. S., Lu, N. Y., & Hu, H. N. (2015), Efficiency Evaluation of Business Colleges in Taiwan-An Application of Students’ Performance Attributable to the Decomposition Method, Taipei Economic Inquiry, 51(1),43–87.
Lee, H., & Kim, C. (2014). Benchmarking of service quality with data envelopment analysis. Expert Systems with Applications, 41(8), 3761-3768.
Lee, K., & Choi, K. (2010). Cross redundancy and sensitivity in DEA models. Journal of Productivity Analysis, 34(2), 151-165.
Lee, S. Y., & Kim, J. A. (2015). The Development of Institutional Food-Service Menu with Temple Food. Korean Journal of Community Nutrition, 20(5), 338-350.
Li, Y. (2014), Service is the Most Efficient Measure of Management, Chinese Religions, (1), 49–51.
LI, Y. L., & WU, C. (2014). Random DEA model considering the weak disposability of undesirable outputs. Journal of Management Sciences in China, 9, 002.
Liu, C. C. (2004), Investigating Weight Restriction in Data Envelop Analysis, Chung Hua Journal of Management, 5(2), 93–104.
Liu, F. H. & Peng, H. H. (2004a). DEA and Common Weight Analysis for Ranking DMUs. Computers and Operations Research, submitted.
Liu, J. S., Lu, L. Y., Lu, W. M., & Lin, B. J. (2013). Data envelopment analysis 1978–2010: A citation-based literature survey. Omega, 41(1), 3-15.
Liu, R., Cui, L., Zeng, G., Wu, H., Wang, C., Yan, S., & Yan, B. (2015). Applying the fuzzy SERVQUAL method to measure the service quality in certification & inspection industry. Applied Soft Computing, 26, 508-512.
Lu, W. M., Wang, T. C., & Wu, H. H. (2012), A Study on Performance of National R&D Organizations and Its Determinants, Journal of Management and Systems, 19(3), 561–587.
Lotfi, F. H., Jahanshahloo, G. R., & Esmaeili, M. (2007). Sensitivity analysis of efficient units in the presence of non-discretionary inputs. Applied mathematics and computation, 190(2), 1185-1197.
Malina, M. A., & Selto, F. H. (2001). Communicating and controlling strategy: an empirical study of the effectiveness of the balanced scorecard. Journal of management accounting research, 13(1), 47-90.
Mokhtar, S. B., & Husain, M. Y. (2015). Service Quality of Polytechnic Using Servqual Model for Sustainable Tvet System.
Mozaffari, M. R., Gholami, K., & Dehghand, F. (2009). Sensitivity and stability analysis in DEA on interval data by using MOLP methods. Applied Mathematical Sciences, 3(18), 891-908.
Najafi, S., Saati, S., & Tavana, M. (2015). Data envelopment analysis in service quality evaluation: an empirical study.Journal of Industrial Engineering International, 11(3), 319-330.
Pan, S.C., Liu, S.Y., Peng, C. J., & Wu, P. C. (2011). Local government efficiency evaluation: Consideration of undesirable outputs and super-efficiency. African Journal of Business Management, 5(12), 4746-4754.
Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1985).A conceptual model of service quality and its implications for future research. The Journal of Marketing, 41-50.
Purcărea, V. L., Gheorghe, I. R., & Petrescu, C. M. (2013). The assessment of perceived service quality of public health care services in Romania using the SERVQUAL scale. Procedia Economics and Finance, 6, 573-585.
Rahman, M. S., Khan, A. H., & Haque, M. M.(2012). A conceptual study on the relationship between service quality towards customer satisfaction: Servqual and gronroos''s service quality model perspective. Asian Social Science, 8(13), 201.
Ray, S. C. (2008). The directional distance function and measurement of super-efficiency: an application to airlines data. Journal of the Operational Research Society, 59(6), 788-797.
Roll, Y., Cook, W.D., & Golany, B. (1991). Controlling factor weights in data envelopment analysis. IIE Transactions, 23, 2-9.
Sanei, M., Noori, N., & Saleh, H. (2009). Sensitivity analysis with fuzzy data in DEA. Applied Mathematical Sciences, 3(25), 1235-1241.
Sasser, W. E., Olsen, R. P., & Wyckoff, D. D. (1978). Management of service operations: Text, cases, and readings. Allyn & Bacon.
Seiford, L. M. (1997). A bibliography for data envelopment analysis (1978-1996). Annals of Operations Research, 73, 393-438.
Sexton, T. R., Silkman, R. H., & Hogan, A. J. (1986). Data envelopment analysis: Critique and extensions. New Directions for Program Evaluation, 32 (1986), 73-105.
Shahin, A., & Zairi, M. (2009). Kano model: A dynamic approach for classifying and prioritising requirements of airline travellers with three case studies on international airlines. Total Quality Management, 20(9), 1003-1028.
Shirouyehzad, H., Lotfi, F. H., Arabzad, S. M., & Dabestani, R. (2013). An AHP/DEA ranking method based on service quality approach: a case study in hotel industry. International Journal of Productivity and Quality Management, 11(4), 434-445.
Sinuany-Stern, Z., Mehrez, A. and Barboy, A. (1994). Academic department’s efficiency in DEA. Computers & Operations Research, 21(5), pp.543–56.
Song, D. H. (2013), Using Analytic Hierarchy Process (AHP) in Design Risk Assessment for Sub-Booster Separation System—A Case Study, master’s thesis, Institute of Industrial and Systems Engineering, Chung Yuan Christian University, 1–101。
Song, H. J., Lee, C. K., Park, J. A., Hwang, Y. H., & Reisinger, Y. (2015). The influence of tourist experience on perceived value and satisfaction with temple stays: The experience economy theory. Journal of Travel & Tourism Marketing, 32(4), 401-415.
Su, M. C. (2011), Operational Efficiency of Featured Elementary Schools in New Taipei City: Application of Data Envelopment Analysis, master’s thesis, Program of Master of Education in School Administration, 1–155.
Talluri, S., & Sarkis, J. (1997). Extensions in efficiency measurement of alternate machine component grouping solutions via data envelopment analysis. IEEE Transactions on Engineering Management, 44(3), 299-304.
Tavana, M., Shiraz, R. K., Hatami-Marbini, A., Agrell, P. J., & Paryab, K. (2013). Chance-constrained DEA models with random fuzzy inputs and outputs. Knowledge-Based Systems, 52, 32-52.
Tavana, M., Shiraz, R. K., & Hatami-Marbini, A. (2014). A new chance-constrained DEA model with birandom input and output data. Journal of the Operational Research Society, 65(12), 1824-1839.
Tavassoli, M., Saen, R. F., & Faramarzi, G. R. (2014). A new super-efficiency model in the presence of both zero data and undesirable outputs. Scientia Iranica. Transaction E Industrial Engineering, 21(6), 2360.
Teeroovengadum, V., Kamalanabhan, T. J., & Seebaluck, A. K. (2016). Measuring service quality in higher education: Development of a hierarchical model (HESQUAL). Quality Assurance in Education, 24(2), 244-258.
Theodorakis, N. D., Alexandris, K., Tsigilis, N., & Karvounis, S. (2013). Predicting  spectators’ behavioural intentions in professional football: The role of satisfaction and service quality. Sport Management Review, 16(1), 85-96.
Thompson, R. G., Langemeier, L. N., Lee, C. T., Lee, E., & Thrall, R. M. (1990). The role of multiplier bounds in efficiency analysis with application to Kansas farming. Journal of econometrics, 46(1-2), 93-108.
Thompson, R. G., Singleton, F. D. Jr., Smith, B. A. & Thrall, R. M. (1986). Comparative site evaluations for locating high energy lab in Texas. TIMS Interfaces, 1380-1395.
Thompson, R. G., Singleton Jr, F. D., Thrall, R. M., & Smith, B. A. (1986). Comparative site evaluations for locating a high-energy physics lab in Texas. Interfaces, 16(6), 35-49.
Thrall, R. M. (1996). Duality, classification and slacks in DEA. Annals of Operations Research, 66(2), 109-138.
Ting, C. T., & Huang, C. W. (2012). Measuring the effectiveness of mutual learning for Taiwan’s tourist hotels with the DEA approach. Cornell Hospitality Quarterly, 53(1), 65-74.
Toloo, M. (2014). Selecting and full ranking suppliers with imprecise data: A new DEA method. The International Journal of Advanced Manufacturing Technology, 74(5-8), 1141-1148.
Tracy, D. L., & Chen, B. (2005). A generalized model for weight restrictions in data envelopment analysis. Journal of the Operational Research Society, 56(4), 390-396.
Wang, Y. M., Luo, Y., & Liang, L. (2009). Ranking decision making units by imposing a minimum weight restriction in the data envelopment analysis. Journal of Computational and Applied Mathematics, 223(1), 469-484.
Wen, M., Qin, Z., & Kang, R. (2011). Sensitivity and stability analysis in fuzzy data envelopment analysis. Fuzzy Optimization and Decision Making, 10(1), 1-10.
Williams, E. J. (1959). Regression analysis, 14, wiley.
Wong, Y. H., & Beasley, J. E. (1990). Restricting weight flexibility in data envelopment analysis. Journal of the Operational Research Society, 41(9), 829-835.
Xiang-Juan, L., & Zun-You, K. (2015). Evaluation of Testing Software Program Based on DEA with Fuzzy Window. In 2015 14th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES), 443-446. IEEE.
Yang, Y. H. (2012), Application of the Meta-frontier Approach Measuring on Technical Universities and Colleges Performance, master’s thesis, Institute of Financial Law, Ling Tung University, 1–57.
Xue, M., & Harker, P. T. (2002). Note: ranking DMUs with infeasible super-efficiency DEA models. Management Science, 48(5), 705-710.
Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353.
Zhu, J. (1996). Robustness of the efficient DMUs in data envelopment analysis. European Journal of Operational Research, 90(3), 451-460.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
系統版面圖檔 系統版面圖檔