ارائه یک روش ترکیبی از تحلیل پوششی داده ها و داده کاوی جهت ارزیابی واحدهای تصمیم گیرنده
محورهای موضوعی :
مدیریت صنعتی
Alireza Alinezhad
1
(
Associate Professor, Department of Industrial Engineering, Faculty of Industrial and Mechanical Engineering, Qazvin branch, Islamic Azad University, Qazvin, Iran.
)
Javad Khalili
2
(
Msc. Student, Department of Industrial Engineering, Faculty of Industrial and Mechanical Engineering, Qazvin branch, Islamic Azad University, Qazvin, Iran
)
تاریخ دریافت : 1396/05/18
تاریخ پذیرش : 1396/09/01
تاریخ انتشار : 1396/10/03
کلید واژه:
Data envelopment analysis,
Data mining,
Malmquist Index,
Efficiency,
کارایی,
تحلیل پوششی داده‌ها,
داده‌کاوی,
درخت تصمیم,
شاخص مالم کوئیست,
Decision tree,
شرکت‌های دارویی,
Pharmaceutical Companies,
چکیده مقاله :
کارایی از موضوع‌های مهمی است که علاوه بر مدیران شرکت‌ها و سازمان‌های مختلف، مشتریان استفاده‌کننده از خدمات این شرکت‌ها و سازمان‌ها نیز به آن علاقه‌مند هستند. هدف این پژوهش، بررسی کارایی شرکت‌های داروسازی پذیرفته‌شده در سازمان بورس اوراق بهادار با استفاده از تحلیل پوششی داده‌ها و سپس ارائه قواعدی با استفاده از داده کاوی است. شاخص مالم کوئیست با امکان‌پذیر ساختن ترکیب مشاهدات سری زمانی و مقطعی، تا حدودی مشکل ناکافی بودن مشاهدات را برطرف می‌کند و برای یافتن روندهای عملکرد یک واحد در طول زمان مفید است. در این پژوهش، ارزیابی کارایی 22 شرکت داروسازی پذیرفته‌شده در سازمان بورس اوراق بهادار با توجه به ورودی‌ها و خروجی‌ها و با استفاده از شاخص مالم کوئیست ورودی محور در طول سال‌های 1395-1391 صورت گرفته است و سپس نتایج حاصل از آن به عنوان برچسب دسته واحدهای تصمیم‌گیرنده که در واقع ورودی روش درخت تصمیم است، مورد استفاده قرار می‌گیرند. درنهایت، با استفاده از درخت تصمیم، قواعد مستتر در داده‌ها استخراج می‌شود.
چکیده انگلیسی:
Efficiency is an important issue for the managers of different companies and organizations, as well as customers who are interested in services of these companies and organizations. The aim of this research is to study the efficiency of pharmaceutical companies accepted in stock exchange organization using Data Envelopment Analysis (DEA) and then, providing some rules using the decision tree. The Malmquist index partly resolves the problem of inadequacy of the observations by enabling the combination of time-series and cross-sectional observations. This method acts on the basis of moving average and it is of use for finding performance trends of a unit during the time. In this research, regarding the inputs and outputs and using the Malmquist index, efficiency evaluation of 22 pharmaceutical companies accepted in stock exchange organization has been done in the state of constant returns to scale during 2012-2016, and the results obtained were used as the class label of Decision-Making Units (DMUs) which are in fact inputs of decision tree method. Finally, using the decision tree, rules implicit in the data, are extracted.
منابع و مأخذ:
Ajali, M. & Safari, H. (2009). Evaluation of the decision-making units using the combined model of neural networks predicting performance and data envelopment analysis (Case study: National Gas Company of Iran). Industrial Engineering Journal, 45(1), 29-13.
Caves, D.W., Christensen L.R. & Diewert, W. E. (1982). The economic theory of index numbers and the measurement of input, output and productivity. Econometrica, 50, 1393–1414.
Charnes, A., Clark, C. T., Cooper, W. W. & Golany, B. (1985). A Developmental Study of Data Envelopment Analysis in Measuring the Efficiency of Maintenance Units in the US Air Forces. Annals of Operations Research, 2, 95-112.
Chiang, T. C., Cheng, P. Y., & Leu, F. Y. (2017). Prediction of technical efficiency and financial crisis of Taiwan’s information and communication technology industry with decision tree and DEA. Soft Computing, 21(18), 5341-5353.
Gholami, P., Najafi, M. & Najafi, M. (2012). Provide a new method using data envelopment analysis model and entropy to select the attribute in data mining. 9th International Industrial Engineering Conference, Iran.
Han, J. & Kamber, M. (2001). Data Mining: Concepts and Techniques. San Diego Academic Press.
Jahanateghi, M.A., Holy, V., Vaez Ghasemi, M. (2011). Multistage Malmquist Productivity Index. Research Journal in Operations and Applications, 8(4), 70-59.
Jenhani, I.; Amor, N. B. & Elouedi, Z. (2008). Decision Trees as Possibilistic Classifiers. International Journal of Approximate Reasoning, 48(3), 784-807.
Lee, S. (2010). Using Data Envelopment Analysis and Decision Trees for Efficiency Analysis and Recommendation of B2C Controls. Journal of Decision Support Systems, 49, 486-497.
Malmquist, S. (1953). Index numbers and indifference surfaces. Trabajos de Estatistica, 4, 209–242.
Mohan, T. T., & Ray, S. C. (2004). Productivity growth and efficiency in Indian banking: a comparison of public, private, and foreign banks. Economics working papers, 1-25.
Rahimi, I. & Behmanesh, R. (2012). Improve Poultry Farm Efficiency in Iran: Using Combination Neural Networks, Decision Trees, and Data Envelopment Analysis (DEA). International Journal of Applied Operational Research, 2(3), 69-84.
Samoilenko, S. & Osei-Bryson, K. M. (2013). Using Data Envelopment Analysis (DEA) for Monitoring Efficiency-based Performance of Productivity-drive Organizations: Design and Implementation of a Decision Support Systems. Omega, 41, 131-142.
Seol, H.; Choi, J.; Park, G. & Park, Y. (2007). A Framework for Benchmarking Service Process Using Data Envelopment Analysis and Decision Tree. Journal of Expert Systems With Applications, 32, 432-440.
Shahi Beyk, A. (2015). Human Resource Performance Evaluation Using Data Envelopment Data Enhancement Results: A Case Study in a Holding Company. International Conference on Advanced Research in Industrial Management and Engineering, Iran.
Siadati, S, Tarokh, M, & Soleimani, M. (2013). Applying data mining methods to enhance the safety of animal food product. Iranian Journal of Nutrition Sciences & Food Technology. 7 (5), 643-650.
Sohn, S. & Moon, T. (2004). Decision Tree Based on Data Envelopment Analysis for Effective Technology Commercialization. Expert Systems With Applications, 26, 279-284.
Toloo, M.; Sohrabi, B. & Nalchigar, S. (2009). A New Method for Ranking Discovered Rules from Data Mining by DEA. Journal of Expert Systems With Applications, 36(4), 8503-8508.
Tsai, Ch. & Tsai, J. (2010). Performance Evaluation of the Judicial System in Taiwan Using Data Envelopment Analysis and Decision Trees. In Computer Engineering and Applications (ICCEA), 2010 Second International Conference on, 2, 290-294.
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Ajali, M. & Safari, H. (2009). Evaluation of the decision-making units using the combined model of neural networks predicting performance and data envelopment analysis (Case study: National Gas Company of Iran). Industrial Engineering Journal, 45(1), 29-13.
Caves, D.W., Christensen L.R. & Diewert, W. E. (1982). The economic theory of index numbers and the measurement of input, output and productivity. Econometrica, 50, 1393–1414.
Charnes, A., Clark, C. T., Cooper, W. W. & Golany, B. (1985). A Developmental Study of Data Envelopment Analysis in Measuring the Efficiency of Maintenance Units in the US Air Forces. Annals of Operations Research, 2, 95-112.
Chiang, T. C., Cheng, P. Y., & Leu, F. Y. (2017). Prediction of technical efficiency and financial crisis of Taiwan’s information and communication technology industry with decision tree and DEA. Soft Computing, 21(18), 5341-5353.
Gholami, P., Najafi, M. & Najafi, M. (2012). Provide a new method using data envelopment analysis model and entropy to select the attribute in data mining. 9th International Industrial Engineering Conference, Iran.
Han, J. & Kamber, M. (2001). Data Mining: Concepts and Techniques. San Diego Academic Press.
Jahanateghi, M.A., Holy, V., Vaez Ghasemi, M. (2011). Multistage Malmquist Productivity Index. Research Journal in Operations and Applications, 8(4), 70-59.
Jenhani, I.; Amor, N. B. & Elouedi, Z. (2008). Decision Trees as Possibilistic Classifiers. International Journal of Approximate Reasoning, 48(3), 784-807.
Lee, S. (2010). Using Data Envelopment Analysis and Decision Trees for Efficiency Analysis and Recommendation of B2C Controls. Journal of Decision Support Systems, 49, 486-497.
Malmquist, S. (1953). Index numbers and indifference surfaces. Trabajos de Estatistica, 4, 209–242.
Mohan, T. T., & Ray, S. C. (2004). Productivity growth and efficiency in Indian banking: a comparison of public, private, and foreign banks. Economics working papers, 1-25.
Rahimi, I. & Behmanesh, R. (2012). Improve Poultry Farm Efficiency in Iran: Using Combination Neural Networks, Decision Trees, and Data Envelopment Analysis (DEA). International Journal of Applied Operational Research, 2(3), 69-84.
Samoilenko, S. & Osei-Bryson, K. M. (2013). Using Data Envelopment Analysis (DEA) for Monitoring Efficiency-based Performance of Productivity-drive Organizations: Design and Implementation of a Decision Support Systems. Omega, 41, 131-142.
Seol, H.; Choi, J.; Park, G. & Park, Y. (2007). A Framework for Benchmarking Service Process Using Data Envelopment Analysis and Decision Tree. Journal of Expert Systems With Applications, 32, 432-440.
Shahi Beyk, A. (2015). Human Resource Performance Evaluation Using Data Envelopment Data Enhancement Results: A Case Study in a Holding Company. International Conference on Advanced Research in Industrial Management and Engineering, Iran.
Siadati, S, Tarokh, M, & Soleimani, M. (2013). Applying data mining methods to enhance the safety of animal food product. Iranian Journal of Nutrition Sciences & Food Technology. 7 (5), 643-650.
Sohn, S. & Moon, T. (2004). Decision Tree Based on Data Envelopment Analysis for Effective Technology Commercialization. Expert Systems With Applications, 26, 279-284.
Toloo, M.; Sohrabi, B. & Nalchigar, S. (2009). A New Method for Ranking Discovered Rules from Data Mining by DEA. Journal of Expert Systems With Applications, 36(4), 8503-8508.
Tsai, Ch. & Tsai, J. (2010). Performance Evaluation of the Judicial System in Taiwan Using Data Envelopment Analysis and Decision Trees. In Computer Engineering and Applications (ICCEA), 2010 Second International Conference on, 2, 290-294.
