Two Alternative Models for Ranking Efficient Units and Benchmarking New Units in Data Envelopment Analysis

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：18

、訪客IP：3.14.143.149

姓名

于世恒(Shih-Heng Yu) 查詢紙本館藏

畢業系所

企業管理學系

論文名稱

(Two Alternative Models for Ranking Efficient Units and Benchmarking New Units in Data Envelopment Analysis)

相關論文

★ 從生態共生觀點發展組織合作模式	★ 影響產業垂直分工因素之探討
★ 發展台籍專業管家的人力派遣模式	★ 影響軍事機關審計品質之關鍵性因素及其相對效率衡量
★ 台灣光碟片設備供應商競爭策略分析	★ 國防科技研發機構組織向心力之研究
★ 衡量半導體零組件通路商之相對經營績效	★ 衡量半導體晶圓代工廠商生產單位之營運績效
★ 商業銀行營運型態與組織調整對分行營運效率的影響	★ 發展行動條碼為基礎的隨傳隨用視訊平台之應用
★ 飛機引擎定子零件維修之訂價調整方式	★ 國際化、創新活動與公司績效：台灣上市公司董事會結構論析
★ 探討網際網路事件達成關鍵多數之影響因素	★ 生態循環農業產銷策略個案研究
★ 探討以政策規範高耗能產業參與再生能源開發之綜效－以太陽光電發電系統為例	★ 適地性服務(LBS)之顧客體驗整合性設計—以中華電信行動導遊為例

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 ( 永不開放)

摘要(中)

資料包絡分析(Data Envelopment Analysis, DEA)係廣為使用的研究方法，能評估具多投入及多產出決策單位(Decision-making Units, DMUs)的相對效率。然而，資料包絡分析存有兩個主要缺陷。其一係未能提供足夠的績效資訊用以排序有效率單位；其二則為難以建構完善的卓越標準(standard of excellence)用以標竿評選新單位。本研究基於非輻射型(Non-radial)架構，嘗試發展兩個相應的資料包絡分析替代模式，以解決上述缺陷。針對排序有效率單位議題，本研究提出雙效率前緣模式，同時融入最優與最劣效率前緣進行效率測量。有效率單位規劃求解之效率值愈高，意味著其具備愈好的非劣水準。與超效率模式比較結果顯示，雙效率前緣模式能(1)避免無可行解問題、(2)提供更穩健效率值及(3)檢視有效率單位的非劣特性。本研究以2015年台灣固體廢棄物(Municipal Solid Waste, MSW)資源回收資料為例，應用雙效率前緣模式評估20縣市政府廢棄物資源回收績效。其中，原近半數(45%)難以區別的有效率縣市政府被依其非劣水準進行排序。而透過差額分析，研究發現有效率縣市政府的非劣水準主要源自於人均預算與設備剩餘所導致。有關標竿評選新單位議題，本研究擴充Zhu (2002)輻射型(Radial)標竿模式至非輻射型標竿模式。所提非輻射型標竿模式為一階混合0-1線性規劃問題，而非植基於差額為基礎模式(Slacks-based measure, SBM)與超效率差額為基礎模式(Super SBM)的二階段方法。研究發現非輻射型標竿模式具以下優勢:(1)考慮投入與產出差額、(2)當新單位凌駕標竿，避免無可行解問題、(3)求解之標竿與投影點具備柏瑞圖最適效率(Pareto efficiency)及(4)可評價新單位為強凌駕或弱凌駕特性。本研究以2015-2016美國職業籃球聯賽(National Basketball Association, NBA)自由球員標竿評選為例，檢視模式的應用性。研究結果除表明非輻射型標竿模式能揭露候選自由球員與既有球員間的績效差距，作為球隊聘僱球員的參酌依據，更實證本研究於運動科學領域之貢獻。

摘要(英)

Data envelopment analysis (DEA) is a widely used approach to measure the relative efficiency of peer decision-making units (DMUs). However, when implementing DEA, two primary issues arise. One is lack of offering enough information for ranking the efficient DMUs, and the other is failure to build a proper standard of excellence for benchmarking the new DMUs. In order to overcome these two issues, this dissertation attempts to develop two alternative models based on the non-radial framework of DEA. For ranking the efficient DMUs, a dual frontiers model that considers not only the best frontier, but also the worst frontier is proposed. The higher the efficiency of efficient DMUs implies the better its non-inferior level. Differ from the well-known super-efficiency model, the dual frontiers model can:(1) avoid the problem of infeasibility, (2) provide more robust efficiency score, and (3) further examine the non-inferiority for efficient DMUs. The proposed model is applied to a Municipal Solid Waste (MSW) recycling data in Taiwan during the year 2015, where nine out of 20 administrative regions originally deemed as commensurate are ranked depending on their non-inferior levels. Furthermore, this study found that the main sources of non-inferiority arise from the per capita budget and equipment. On the side of benchmarking the new DMUs, this dissertation extends the Zhu’s (2002) work by developing a non-radial benchmarking model that is unified as a mixed 0-1 linear program instead of a cumbersome two-stage approach combining both slacks-based measure (SBM) and super SBM in sequence. The proposed model has following merits: (1) it takes slacks into assessment, (2) it overcomes the infeasibility problem, (3) it guarantees that the benchmarks and projections are strongly Pareto-efficient, and (4) it classifies outperformance status into strong and weak. We illustrate the proposed model with a National Basketball Association (NBA) player recruitment case in the 2015-2016 regular season. The results not only show that our model can provide new insights into gap between candidates and the team’s benchmark players for a NBA team, but also evidence the salient contributions of this dissertation in sport science domain.

關鍵字(中)

★ 資料包絡分析
★ 超效率模式
★ 最劣效率前緣
★ 差額為基礎模式
★ 固體廢棄物資源回收
★ 球員標竿評選
★ 美國職業籃球聯賽

關鍵字(英)

★ Data Envelopment Analysis
★ Super-efficiency Model
★ Worst Frontier
★ Slacks-based Measure
★ MSW Recycling
★ Player Benchmarking
★ NBA

論文目次

摘要 i
ABSTRACT ii
TABLE OF CONTENTS iii
LIST OF TABLES v
LIST OF FIGURES vi
CHAPTER 1 INTRODUCTION 1
1.1 Background and Motivation 1
1.2 Research Objectives 4
1.3 Organization of the Dissertation 5
CHAPTER 2 LITERATURE REVIEW 7
2.1 DEA with Radial Measure 7
2.2 DEA with Additive Measure 13
2.3 DEA with ERM and SBM 15
CHAPTER 3 THE ALTERNATIVE DEA MODEL FOR RANKING EFFICIENT DECISION MAKING UNITS 19
3.1 Super-efficiency Model and its Deficiencies 19
3.2 Developing a Dual Frontiers Model to Differentiate Efficient DMUs 22
3.3 Comparison of Numerical Example 26
3.4 Empirical Case in Taiwan: Recycling System of Municipal Solid Waste 30
CHAPTER 4 THE ALTERNATIVE DEA MODEL FOR BENCHMARKING NEW DECISION MAKING UNITS 37
4.1 Radial Benchmarking Model and its Deficiencies 37
4.2 Developing a Benchmarking Model with SBM to Benchmark New DMUs 41
4.3 Comparison of Numerical Example 48
4.4 Empirical Case in United States: Player Recruitment of National Basketball Association 54
CHAPTER 5 CONCLUSIONS 69
5.1 Concluding Remarks 69
5.2 Recommendations for Future Research 70
REFERENCES 71

參考文獻

〔1〕Andersen, P., & Petersen, N. C. (1993). A procedure for ranking efficient units in data envelopment analysis. Management Science, 39(10), 1261-1264.
〔2〕Ashrafi, A., Seow, H., Lee, L. S., & Lee, C. G. (2013). The efficiency of the hotel industry in singapore. Tourism Management, 37, 31-34.
〔3〕Avkiran, N. K., & Cai, L. (2014). Identifying distress among banks prior to a major crisis using non-oriented super-SBM. Annals of Operations Research, 217(1), 31-53.
〔4〕Avkiran, N. K., & Rowlands, T. (2008). How to better identify the true managerial performance: State of the art using DEA. Omega, 36(2), 317-324.
〔5〕Banker, R. D., & Chang, H. (2006). The super-efficiency procedure for outlier identification, not for ranking efficient units. European Journal of Operational Research, 175(2), 1311-1320.
〔6〕Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9): 1078-1092.
〔7〕Barros, C. P., & Leach, S. (2006). Performance evaluation of the english premier football league with data envelopment analysis. Applied Economics, 38(12), 1449-1458.
〔8〕Camp, R. C. (1989). Benchmarking: The search for best practices that lead to superior performance. Quality Progress, 22 (1), 61-68.
〔9〕Chang, D., Liu, W., & Yeh, L. (2013). Incorporating the learning effect into data envelopment analysis to measure MSW recycling performance. European Journal of Operational Research, 229(2), 496.
〔10〕Charnes A, Haag S, Jaska P, & Semple J (1992). Sensitivity of efficiency classifications in the additive-model of data envelopment analysis. International Journal of Systems Science, 23(5), 789-798.
〔11〕Charnes, A. & Cooper, W. W. (1962). Programming with linear fractional functionals. Naval Research Logistics Quarterly, 15, 333-334.
〔12〕Charnes, A., Clark, C. T., Cooper, W. W., & Golany, B. (1984). A development study of data envelopment analysis in measuring the efficiency of maintenance units in US air forces. Annals of Operation Research, 2(1), 95-112.
〔13〕Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444.
〔14〕Charnes, A., Cooper, W. W., Huang, Z. M., & Sun, D. B. (1990). Polyhedral cone-ratio DEA models with an illustrative application to large commercial banks. Journal of Econometrics, 46 (1-2), 73-91.
〔15〕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.
〔16〕Charnes, A., Cooper, W.W., Golany, B., Seiford, L, & Stutz, J. (1985). Foundations of data envelopment analysis for Pareto-Koopmans efficient empirical production functions. Journal of Econometrics, 30 (1), 91-107.
〔17〕Chen, C. M. (2013). Super efficiencies or super inefficiencies? Insights from a joint computation model for slacks-based measures in DEA. European Journal of Operational Research, 226(2), 258-267.
〔18〕Chen, C. M., Du, J., Huo, J., & Zhu, J.(2012). Undesirable factors in integer-valued DEA: Evaluating the operational efficiencies of city bus systems considering safety records. Decision Support Systems, 54(1), 330-335.
〔19〕Chen, Y. (2005). Measuring super-efficiency in DEA in the presence of infeasibility. European Journal of Operational Research, 161(2), 545-551.
〔20〕Cook, W. D., Harrison, J., Imanirad, R., Rouse, P., & Zhu, J. (2013). Data envelopment analysis with nonhomogeneous DMUs. Operations Research, 61(3), 666-676.
〔21〕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.
〔22〕Cook, W. D., Roll, Y., & Kazakov, A. (1990). A DEA model for measuring the relative efficiency of highway maintenance patrols. INFOR, 28(2), 113.
〔23〕Cook, W. D., Seiford, L. M., & Zhu, J. (2004). Models for performance benchmarking: Measuring the effect of e-business activities on banking performance. Omega, 32(4), 313-322.
〔24〕Cook, W. D., Zhu, J., Bi, G., & Yang, F. (2010). Network DEA: Additive efficiency decomposition. European Journal of Operational Research, 207(2), 1122-1129.
〔25〕Cooper, R. (1998). Benchmarking new product performance: Results of the best practices study. European Management Journal, 16(1), 1-17.
〔26〕Cooper, W. W., Ruiz, J. L., & Sirvent, I. (2009). Selecting non-zero weights to evaluate effectiveness of basketball players with DEA. European Journal of Operational Research, 195(2), 563-574.
〔27〕Cooper, W. W., Seiford, L. M., & Tone, K. (1999). Data envelopment analysis : A comprehensive text with models, applications, references, and DEA-solver software. Hingham: Kluwer Academic Publishers.
〔28〕Cooper, W. W., Seiford, L. M., & Zhu, J. (2011). Handbook on data envelopment analysis. Boston, MA: Springer.
〔29〕Despotis, D. K., Sotiros, D., & Koronakos, G. (2016). A network DEA approach for series multi-stage processes. Omega, 61, 35-48.
〔30〕Drake, L., Simper, R., & Hall, M. J. B. (2006). The impact of macroeconomic and regulatory factors on bank efficiency: A non-parametric analysis of hong kong′s banking system. Journal of Banking & Finance, 30(5), 1443-1466.
〔31〕Du, J., Wang, J., Chen, Y., Chou, S., & Zhu, J. (2014). Incorporating health outcomes in pennsylvania hospital efficiency: An additive super-efficiency DEA approach. Annals of Operations Research, 221(1), 161-172.
〔32〕Dyson, R. G., Allen, R., Camanho, A. S., Podinovski, V. V., Sarrico, C. S., & Shale, E. A. (2001). Pitfalls and protocols in DEA. European Journal of Operational Research, 132(2), 245-259.
〔33〕Elmuti, D., & Kathawala, Y. (1997). An overview of benchmarking process: A tool for continuous improvement and competitive advantage. Benchmarking for Quality Management & Technology, 4(4), 229.
〔34〕Entani, T., Maeda, Y., & Tanaka, H. (2002). Dual models of interval DEA and its extension to interval data. European Journal of Operational Research, 136(1), 32-45.
〔35〕Espitia‐Escuer, M., & García‐Cebrián, L. I. (2006). Performance in sports teams: Results and potential in the professional soccer league in spain. Management Decision, 44(8), 1020-1030.
〔36〕Fang, H., Lee, H., Hwang, S., & Chung, C. (2013). A slacks-based measure of super-efficiency in data envelopment analysis: An alternative approach. Omega, 41(4), 731-734.
〔37〕Färe, R., & Grosskopf, S. (2000). Network DEA. Socio-Economic Planning Sciences, 34(1), 35-49.
〔38〕Färe, R., Grosskopf, S., Norris, M., & Zhang, Z. (1994). Productivity growth, technical progress, and efficiency change in industrialized countries. The American Economic Review, 84(1), 66-83.
〔39〕Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society: Series A (General), 120(3), 253-290.
〔40〕Griffin, A. (1997). PDMA research on new product development practices: Updating trends and benchmarking best practices. The Journal of Product Innovation Management, 14(6), 429-458.
〔41〕Grigoroudis, E., Litos, C., Moustakis, V. A., Politis, Y., & Tsironis, L. (2008). The assessment of user-perceived web quality: Application of a satisfaction benchmarking approach. European Journal of Operational Research, 187(3), 1346-1357.
〔42〕Haas, D. J. (2003). Productive efficiency of english football teams: A data envelopment analysis approach. Managerial and Decision Economics, 24(5), 403-410.
〔43〕Howard, L. W., & Miller, J. L. (1993). Fair pay for fair play: Estimating pay equity in professional baseball with data envelopment analysis. The Academy of Management Journal, 36(4), 882-894.
〔44〕Išoraite, M. (2004). Benchmarking methodology in a transport sector. Transport, 19(6), 269.
〔45〕Kao, C. (2009). Efficiency measurement for parallel production systems. European Journal of Operational Research, 196(3), 1107-1112.
〔46〕Kao, C. (2012). Efficiency decomposition for parallel production systems. Journal of the Operational Research Society, 63(1), 64-71.
〔47〕Kao, C. (2013). Dynamic data envelopment analysis: A relational analysis. European Journal of Operational Research, 227(2), 325-330.
〔48〕Kao, C., & Hwang, S. (2008). Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan. European Journal of Operational Research, 185(1), 418-429.
〔49〕Kao, C., & Liu, S. (2016). A parallel production frontiers approach for intertemporal efficiency analysis: The case of Taiwanese commercial banks. European Journal of Operational Research, 255(2), 411-421.
〔50〕Klopp G.A. (1985). The analysis of the efficiency of production system with multiple inputs and outputs. Ph.D. dissertation. University of Illinois, Industrial and System Engineering College, Chicago.
〔51〕Lee, B. L., & Worthington, A. C. (2013). A note on the ‘Linsanity’ of measuring the relative efficiency of national basketball association guards. Applied Economics, 45(29), 4193-4202.
〔52〕Lewis, H. F., & Sexton, T. R. (2004). Network DEA: Efficiency analysis of organizations with complex internal structure. Computers and Operations Research, 31(9), 1365-1410.
〔53〕Lichtenthaler, U., & Ernst, H. (2007). External technology commercialization in large firms: Results of a quantitative benchmarking study. R&D Management, 37(5), 383-397.
〔54〕Liu, F. H., & Chen, C. L. (2009). The worst-practice DEA model with slack-based measurement. Computers and Industrial Engineering, 57(2), 496-505.
〔55〕Lovell, C. A. K., & Rouse, A. P. B. (2003). Equivalent standard DEA models to provide super-efficiency scores. Journal of the Operational Research Society, 54(1), 101-108.
〔56〕Lozano, S. (2015). Alternative SBM model for network DEA. Computers & Industrial Engineering, 82, 33-40.
〔57〕Matook, S., Lasch, R., & Tamaschke, R. (2009). Supplier development with benchmarking as part of a comprehensive supplier risk management framework. International Journal of Operations & Production Management, 29(3), 241-267.
〔58〕Miciak, A., & Desmarais, M. (2001). Benchmarking service quality performance at business-to-business and business-to-consumer call centers. Journal of Business & Industrial Marketing, 16(5), 340-353.
〔59〕Mirdehghan, S. M., & Fukuyama, H. (2016). Pareto–Koopmans efficiency and network DEA. Omega, 61, 78-88.
〔60〕Moreno, P., & Lozano, S. (2015). Estimation of productivity change of NBA teams from 2006-07 to 2012-13 seasons. International Journal of Sport Finance, 10(3), 217.
〔61〕Morita, H., Hirokawa, K., & Zhu, J. (2005). A slack-based measure of efficiency in context-dependent data envelopment analysis. Omega, 33(4), 357-362.
〔62〕Morling, P., & Tanner, S. (2000). Benchmarking a public service business management system. Total Quality Management, 11(4-6), 417-426.
〔63〕Paradi, J. C., Asmild, M., & Simak, P. C. (2004). Using DEA and worst practice DEA in credit risk evaluation. Journal of Productivity Analysis, 21(2), 153-165.
〔64〕Pastor, J. T., Ruiz, J. L., & Sirvent, I. (1999). An enhanced DEA russell graph efficiency measure. European Journal of Operational Research, 115(3), 596-607.
〔65〕Radovanovic, S., Radojicic, M., & Savic, G. (2014). Two-phased DEA-MLA approach for predicting efficiency of NBA players. Yugoslav Journal of Operations Research, 24(3), 347-358.
〔66〕Roll, Y., Cook, W. D., & Golany, B. (1991). Controlling factor weights in data envelopment analysis. IIE Transactions, 23(1), 2-9.
〔67〕Seiford, L. M., & Zhu, J. (1999). Infeasibility of super-efficiency data envelopment analysis models. INFOR, 37(2), 174-187.
〔68〕Seiford, L. M., & Zhu, J. (1999). Profitability and marketability of the top 55 U.S. commercial banks. Management Science, 45(9), 1270-1288.
〔69〕Sengupta, J. (2000). Dynamic and stochastic efficiency analysis: Economics of data envelopment analysis. River Edge, NJ;Singapore;: World Scientific.
〔70〕Sexton, T. R., & Lewis, H. F. (2003). Two-stage DEA: An application to major league baseball. Journal of Productivity Analysis, 19(2-3), 227-249.
〔71〕Sexton, T., Silkman, R., & Hogan, A. (1986). Data envelopment analysis: critique and extensions. In Silkman R. (ed.) Measuring efficiency: An assessment of data envelopment analysis. San Francisco: Jossey-Bass, 73-105.
〔72〕Shen, W., Zhang, D., Liu, W., & Yang, G. (2016). Increasing discrimination of DEA evaluation by utilizing distances to anti-efficient frontiers. Computers & Operations Research, 75, 163-173.
〔73〕Sueyoshi, T., & Honma, T. (2003). DEA network computing in multi-stage parallel processes. International Transactions in Operational Research, 10(3), 217-244.
〔74〕Sueyoshi, T., Ohnishi, K., & Kinase, Y. (1999). A benchmark approach for baseball evaluation. European Journal of Operational Research, 115(3), 429-448.
〔75〕Talluri, S., & Narasimhan, R. (2004). A methodology for strategic sourcing. European Journal of Operational Research, 154(1), 236-250.
〔76〕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.
〔77〕Thompson, R. G., Singleton, 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.
〔78〕Thrall, R. M. (1996). Duality, classification and slacks in DEA. Annals of Operations Research, 66, 109-138.
〔79〕Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130(3), 498-509.
〔80〕Tone, K., & Tsutsui, M. (2009). Network DEA: A slacks-based measure approach. European Journal of Operational Research, 197(1), 243-252.
〔81〕Tone, K., & Tsutsui, M. (2010). Dynamic DEA: A slacks-based measure approach. Omega, 38(3), 145-156.
〔82〕Wan, H., & Frank Chen, F. (2008). A leanness measure of manufacturing systems for quantifying impacts of lean initiatives. International Journal of Production Research, 46(23), 6567-6584.
〔83〕Wang, Y. M., Chin, K. S., & Yang, J. B. (2007). Measuring the performances of decision-making units using geometric average efficiency. Journal of the Operational Research Society, 58(7), 929-937.
〔84〕Xue, M., & Harker, P. T. (2002). Note: Ranking DMUs with infeasible super-efficiency DEA models. Management Science, 48(5), 705-710.
〔85〕Yang, C., Lin, H., & Chen, C. (2014). Measuring the efficiency of NBA teams: Additive efficiency decomposition in two-stage DEA. Annals of Operations Research, 217(1), 565-589.
〔86〕Zhu J. (2002). Quantitative Models for Performance Evaluationand Benchmarking: Data Envelopment Analysis with Spreadsheets, Boston: Kluwer Academic Publishers.
〔87〕Zhu, J. (1996). Robustness of the efficient DMUs in data envelopment analysis. European Journal of Operational Research, 90(3), 451-460.

指導教授

張東生(Dong-Shang Chang)

審核日期

2017-6-5

推文