رتبه‌بندی شرکت‌های صنعت داروسازی با رویکرد تلفیقی تصمیم‌گیری چند معیاره و الگوریتم ژنتیک

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشیار و عضو هیئت علمی دانشگاه الزهرا

2 کارشناس ارشد مدیریت صنعتی

3 کارشناس ارشد مدیریت صنعتی، دانشگاه تهران

چکیده

مقدمه: هدف از این پژوهش رتبه‌بندی شاخص‌های عملکرد مالی و رتبه‌بندی شرکت‌های داروسازی به کمک رویکرد تلفیقی الگوریتم ژنتیک و تصمیم‌گیری چند شاخصه و مقایسه نتایج با روش تاپسیس است.
روش پژوهش: در این پژوهش، ابتدا، به شناسایی مهم‌ترین شاخص‌های مالی به کمک روش‌ آنتروپی شانون پرداخته و سپس شرکت‌های داروسازی با کمک رویکرد تلفیقی رتبه‌بندی شدند و نتایج با روش تاپسیس مقایسه شد. به این منظور از پنج شاخص اصلی (شاخص‌های اهرمی، نسبت کارایی، نسبت ارزش بازار، نسبت نقدینگی و نسبت فعالیت) و 24 شاخص فرعی دخیل در رتبه‌بندی عملکرد مالی شرکت‌ها، استفاده شده است. جامعه آماری این پژوهش شامل کلیه شرکت‌های داروسازی پذیرفته شده در بورس اوراق بهادار تهران از تاریخ 29/12/1385 تا 29/12/1390 است و داده‌های مورد استفاده از صورت‌های مالی و اظهارنامه‌های قانونی شرکت‌ها بر اساس اطلاعات سال 1390 استخراج شده است.
یافته‌ها: یافته‌های پژوهش نشان می‌دهد که مهم‌ترین معیارها گردش حساب، سود هر سهم و درصد پرداخت سود است. هم‌چنین، شرکت داروسازی سبحان از نظر کل معیارها برترین گزینه به حساب آمده و به عنوان بهترین شرکت در صنعت داروسازی انتخاب شد و سپس شرکت مواد اولیه داروپخش و البرز دارو کم‌ترین فاصله را با داروسازی سبحان داشته و در رتبه‌های بعدی قرار می‌گیرند.
نتیجه‌گیری: در 75 درصد موارد مشاهده شده نتایج حاصل از الگوریتم ژنتیک با روش تاپسیس یکسان است. هم‌چنین، الگوریتم ژنتیک افزون‌بر رتبه‌بندی شرکت‌های داروسازی، فاصله شرکت‌ها را از یکدیگر مشخص می‌کند.

کلیدواژه‌ها

عنوان مقاله [English]

The Ranking of Pharmaceutical Companies using an Integrative Multi-Standard Decision-Making and Genetic Algorithm

نویسندگان [English]

  • E. Abasi 1
  • S. H. Ahmadi 2
  • E. Heidari 3
1 1Associate Professor and Faculty member of Alzahra University
2 M. A. in Industrial Management
3 M. A. in Industrial Management, Tehran University
چکیده [English]

Introduction: The purpose of this study is to rank the financial performance measures and to rank the pharmaceutical companies using an integrative multi-criteria decision-making and genetic algorithm, and finally to compare the obtained results with those of Topsis technique.
Methods: In this study, first, the most important financial measures were identified using Shannon Entropy method. Second, the pharmaceutical companies were ranked using an integrative approach, and the results were compared to those of Topsis method. To this aim, five major measures (including leverage indicator, efficiency ratio, market value ratio, liquidity ratio and activity ratio) and 24 secondary indicators involved in ranking financial performances of companies were used. The population of this study includes all the pharmaceutical companies listed in Tehran Stocks Exchange from 1385/12/29 to 1390/12/29. The data used in this study comprised of financial statements and the legal declarations of the companies in 1390.
Results: The results of the study indicate that the most important measures are account turnover, earning per share, and the percentage of the dividend. Given all the measures, Sobhan pharmaceutical company was chosen as the best one and Daroupakhsh and Alborzdarou were among the next two best companies, respectively.
Conclusion: The results obtained through Genetic Algorithm are similar to those obtained through Topsis technique in 75 % of observed cases. Moreover, the Genetic Algorithm is capable of not only ranking the pharmaceutical companies but also the determining the distance between them.

کلیدواژه‌ها [English]

  • Multi-criterion Decision-making
  • Ranking
  • genetic algorithm
  • TOPSIS
  • pharmaceutic industry

مراجع

1        Ballestero, E.; Günther, M.; Pla-Santamaria, D.; and C. Stummer (2007). “Portfolio Selection Under Strict Uncertainty: A Multi-Criteria Methodology and it's Application to the Frankfurt and Vienna Stock Exchanges”. European Journal of Operational Research, Vol. 181, No. 3, pp. 1476-1487.
2        Yu, J. R. and W. Y. Lee (2011). “Portfolio Rebalancing Model Using Multiple Criteria”. European Journal of Operational Research, Vol. 209, No. 2, pp. 166-175.
3        Azizi, H. (2013). “A Review of Pharmaceutic Industry in Iran”. Exchange News. Available at: http://www.boursenews.ir/fa/pages/?cid=99645, [online] [22 June 2013].[In Persian]
4        Hafeziyeh, A. A. (2006). “Capital Market and the Role of Paper Money Exchange in Economy of the Country”. Mesbah Review, Vol. 15, No. 1, pp. 31-50. [In Persian]
5        Shahalizahe, M. and A. Memariyani (2003). “A Mathematical Framework for the Selection Portfolio with Multiple Purposes”. The Iranian Accounting and Auditing Review, Vol. 10, No.1, pp. 83-102. [In Persian]
6        WU, C. and P. Colwell (1987). “Macroeconomic Factors and Stock Returns”. Journal of Financial Research, Vol. 10, No. 2, p. 87.
7        Soukhagiyan, M. A.; Valipour, H.; and L. Fayyaz (2010). “Multiple Indices Decision Making for the Selection of Stock in Stock Exchnge Using Financial Variables”. Financial Engineering and Portfolio Management, Vol. 1, No. 5, pp.35-53. [In Persian]
8        Garkaz, M.; Abbasi, A.; and M. Moghaddasi (2010). “The Election and Optimization of Portfolio Using Genetic Algorithm based on Various Defenitions of Risk”. Industrial Management Quarterly of Humanity Faculty of Islamic Azad University, Sannadaj Branch, Vol. 5, Issue. 11, pp. 115-136. [In Persian]
9        Jaafarpour, F. (1999). “The Development of Intelligent Election Systems of Stock Complex (Problem Solving Related to the Distribution of Intelligent Efforts)”. M. A. Thesis, Management Faculty of Tehran University. [In Persian]
10    Nourbakhsh, A. and R. Tehrani (Translators) (2009). Investment Management. Tehran: Negahe
Danesh Publication. [In Persian]
11    Jahankhani, A. and A. Parsiyan (Translators) (2009). Financial Management, Vol. 1. Tehran: Samt Publication. [In Persian]
12    Hadavi Nejad, M. (2004). “Identifying the Factors Affecting the Election of Stock in Tehran Stock Exchange (Cement Companies) Using MADM Approach”.  M. A. Thesis, Imam Sadegh University. [In Persian]
13    Diakoulaki, O.; Mavrotas, G.; and L. A. Papagyannakis (1992). “A Multicriteria Approach for Evaluating the Performance of Industrial Firms”. Omaga, Vol. 20, No. 4, pp. 467-474.
14    Siskos, Y.; Zopounidis, C.; and A. Pouliezos (1994). “An Integrated DSS for Financing Firms by an Industrial Development Bank in Greece”. Decision Support Systems, Vol. 12, No. 2, pp. 151-168.
15    Samaras, G. D.; Matsatsinis, N. F.; and C. Zopounidis (2008). “A Multicriteria DSS for Stock Evaluation Using Fundamental Analysis”. European Journal of Operational Research, Vol. 187, Issue. 3, 1380-1401.
16    Delbari, M. (2001). “Examining the Influential Indices in the Election of Stock in Tehran Stock Exchange Based on the Process of Hierarchical Analysis”. M. A. Thesis, Isfahan University. [In Persian]
17    Motameni, A. R. and A. R. S. Salim (2012). “Designing a New Model for the Election of Portfolio in Tehran Exchange Stock Using Multi-Criteria Decision Making”. Journal of Industrial Management Perspective, Vol. 2, No. 5, pp. 73-89. [In Persian]
18    Triantaphyllou, E. and G. W. Evans (1999). “Multi-Criteria Decision Making in Industrial Engineering”. Computers and Industrial Engineering, Vol. 37, No. 3, pp. 505-506.
19    Huang, D. K.; Chiu, H. N.; Yeh, R. H.; and J. H. Chang (2009). “A Fuzzy Multi-Criteria Decision Making Approach for Solving a Bi-Objective Personnel Assignment Problem”. Computers and Industrial Engineering, Vol. 56, No. 1, pp. 1-10.
20    Chuu, S. J. (2009). “Selecting the Advanced Manufacturing Technology Using Fuzzy Multiple Attributes Group Decision Making with Multiple Fuzzy Information”. Computers and Industrial Engineering, Vol. 57, No. 3, pp. 1033-1042.
21    Chiadamrong, N. (1999). “An Integrated Fuzzy Multi-Criteria Decision Making Method for Manufacturing Strategies Selection”. Computers and Industrial Engineering, Vol. 37, No. 1, pp. 433-436.
22    Nowak, M. (2007). “Aspiration Level Approach in Stochastic MCDM Problems”. European Journal of Operational Research, 177(3), 1626-1640.
23    Nowak, M. (2006). “INSDECM-An Interactive Procedure for Stochastic Multicriteria Decision Problems”. European Journal of Operational Research, Vol. 175, No. 3, pp. 1413-1430.
24    Nowak, M. (2004). “Preference and Veto Thresholds in Multicriteria Analysis based on Stochastic Dominance”. European Journal of Operational Research, Vol. 158, No. 2, pp. 339-350.
25    Martel, J. M. and K. Zaras (1995). “Stochastic Dominance in Multicriterion Analysis Under Risk”. Theory and Decision, Vol. 39, No. 1, pp. 31-49.
26    Mansour, M. (2006). Modern Research Issues in Operation. First Edition, Tehran: University Publication. [In Persian]
27    Holland, J. H. (1975). Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. Massachusetts: MIT Press.
28    Lee, W. S.; Tzeng, G. H.; Guan, J. L.; Chien, K. T.; and J. M. Huang (2009). “Combined MCDM Techniques for Exploring Stock Selection based on Gordon Model”. Expert Systems with Applications, Vol. 36, No. 3, pp. 6421-6430.
29    Chunhachinda, P.; Dandapani, K.; Hamid, S.; and A. J. Prakash (1997). “Portfolio Selection and Skewness: Evidence from International Stock Markets. Journal of Banking and Finance, Vol. 21, No. 2, pp. 143-167.
30    Xidonas, P.; Mavrotas, G.; Zopounidis, C.; and J. Psarras (2011). “IPSSIS: An Integrated Multicriteria Decision Support System for Equity Portfolio Construction and Selection”. European Journal of Operational Research, Vol. 210, No. 2, pp. 398-409.

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