ارزیابی و اولویت بندی گازهای آلاینده کارخانه سیمان بر اساس مدلهای FDH و CRA
محورهای موضوعی : آمار
فاطمه دادخواه
1
,
محمدرضا مظفری
2
,
جواد گرامی
3
1 - گروه ریاضی، واحد مرودشت، دانشگاه آزاد اسلامی، مرودشت، ایران
2 - گروه ریاضی، واحد شیراز، دانشگاه آزاد اسلامی، شیراز، ایران
3 - گروه ریاضی، واحد شیراز، دانشگاه آزاد اسلامی، شیراز، ایران
کلید واژه: Central resource allocation, Free disposal hull, Data Envelopment Analysis, TOPSIS,
چکیده مقاله :
در بسیاری از کشورها کنترل و کاهش میزان آلودگی های محیط زیست بسیار حائز اهمیت می باشد. در این مقاله بر اساس نظر کارشناسان، گازهای آلاینده در صنعت سیمان مشخص شده و بر اساس مدلهای تحلیل پوششی داده ها کنترل و فیلتر کردن این گازها اولویت‌بندی شده اند. با دو مدل شعاعی و غیرشعاعی پیشنهادی، کاهش مجموع هزینه ها و تاثیرات گازهای آلاینده و با یک مدل غیر شعاعی پیشنهادی دیگر، کاهش هزینه و تاثیرات گازهای آلاینده اولویت بندی شده است. این گازهای آلاینده، بر اساس مدلهای FDH و تخصیص منابع مرکزی، نیز با در نظر گرفتن نوع بیماری و هزینه، اولویت بندی شده است. در خاتمه نتایج اولویت‌بندی گازهای آلاینده صنعت سیمان فارس با سه مدل پیشنهادی و روش تاپسیس ومدل FDH مقایسه شده است.
Control and reduction in environmental pollutants are of a great importance in all countries. In this paper, pollutant gases in the cement industry are analyzed and then prioritized for purposes of control and filtration based on DEA models. Two new radial and non-radial DEA models are proposed to prioritize reduction in total costs and in the effects of pollutant gases. Then, another non-radial model is provided for prioritizing reduction in costs and in the effects of pollutant gases. This prioritization is carried out using free disposal hull (FDH) and central resource allocation (CRA) models considering the types of disease caused by the pollutant gases and the medical treatment costs incurred. Finally, the results of the prioritization of Fars cement industry pollutant gases by all three proposed models are compared with those given by Technique for Order of Preference by Similarity to an Ideal Solution (TOPSIS) method and by FDH models.
[1] Charnes A., Cooper W.W., Rhodes E., 1978, Measuring the efficiency of decision making units, European Jouenal of Operational Research 2, 429-444.
[2] Banker R.D., Charnes A., Cooper W.W., 1984, Some Model for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis, Management Science 30, 1078-1092.
[3] Deprins, D., Simar, L., Tulkens, H., 1984, Measuring labor efficiency inpost offices. In M. Marchand, P. Pestieau, H. Tulkens (Eds.), The performance of public enterprises: Concepts and measurements. Amsterdam: North Holland.
[4] Podinovski, V.V., 2004, On the linearization of reference technologies for testing returns to scale in FDH models, European Journal of Operational Research, 152, 800–802.
[5] Soleimani-damaneh M., Reshadi M., 2007, A polynomial-time algorithm to estimate returns to scale in FDH models, Computers & Operations Research 34, 2168–2176.
[6] Soleimani-damaneh M., Mostafaee A., 2015, Identification of the anchor points in FDH models, European Journal of Operational Research, 246, 936–943.
[7] García-Romero A., Santín D., Sicilia G., 2016, Another brick in the wall: a new ranking of academic journals in Economics using FDH ,Scientometrics, 107, 91–101.
[8] Golany B., Phillips F.Y., Rousseau J. J., 1993, Models for Improved Effectiveness Based on DEA Efficiency Results, IIE Transactions 25(6) 2–10.
[9] Golany B., Tamir E., 1995, Evaluating Efficiency-Effectiveness-Equality Trade-offs: A Data Envelopment Analysis Approach, Management Science 41(7) 1172–1184.
[10] Lozano, S., Villa G., 2004, Centralized Resource Allocation Using Data Envelopment Analysis, Journal of Productivity Analysis, 22, 143–161.
[11] Hwang C.L., Yoon K., 1981, Multiple attribute decision making: Methods and applications, Spring, NewYork.
[12] Chen S., Hwang C., 1992, Fuzzy Multiple Attribute Decision Making Methods and Applications, Springer-Verlag Berlin Heidelberg
[13] Yue Z., 2011, An extended TOPSIS for determining weights of decision makers with interval numbers, Knowledge-Based Systems 24, 146–153.
[14] Behzadian M., Khanmohammadi Otaghsara S., Yazdani M., Ignatius J., 2012, A state-of the-art survey of TOPSIS applications, Expert Systems with Applications 39, 13051–13069.
[15] Shanon C.E., 1948, A mathematical theory of communication, Bell System Technival Journal, 27, 379-423.
[16] World Health Organization, 2011, Air pollution report, An inexact optimization modeling approach for supporting energy systems planning and air pollution mitigation in Beijing city, Energy, 37, 673-688.
[17] Bevilacqua M., Braglia M., 2002, Environmental Efficiency Analysis for ENI Oil refineries, Journal of Cleaner Production, 10(1) 85-92.
[18] Kumar Mandal S., Madheswaran S., 2009, Environmental Efficiency of The Indian Cement Industry: an Interstate Analysis; The Institute for Social and Economic Change, Bangalore, working paper 227.
[19] Benhelal E., Rafiei A., Shamsaei E., 2012, Green Cement Production: Potentials and Achievements, International Journal of Chemical Engineering and Applications, 3(6) 407-409.
[20] Oguntoke O., Awanu A.E., Annegarn H.J., 2012, Impact of cement factory operations on air quality and human health in Ewekoro Local Government Area, South-Western Nigeria, International Journal of Environmental Studies, 69(6), 934–945.
[21] Zand Hessami H., Golsefid-Alavi M., Madan Shekaf S., Kiani Mavi R., 2012, Evaluation of Success Factors of ISO 14001- Based EMS Implementation and Ranking the Cement Industry Using the TOPSIS Method, Journal of Applied Environmental and Biological Sciences, 2(10) 523-530.
[22] Wang K., Yu S., Zhang W., 2013, China’s Regional Energy and Environmental Efficiency: a DEA Window Analysis Based Dynamic Evaluation. Mathematical and Computer Modelling, 58, 1117-1127.
[23] Sana S., Bhat G.A., Mehraj Balkhi H., 2013, Health Risks associated With Workers in Cement Factories, International Journal of Scientific and Research Publications, 3(5) 2250-3153.
[24] Oyinloye M.A., 2015, Environmental Pollution and Health Risks of Residents Living Near Ewekoro Cement Factory, Ewekoro, Nigeria, International Journal of Environmental, Chemical, Ecological, Geological and Geophysical Engineering, 9(2) 108-114.
[25] Lozano S., 2015, A joint-inputs Network DEA approach to production and pollution-generating technologies, Expert Systems with Applications 42, 7960–7968.
[26] Abbasi M., Kaviani M.A., 2016, Operational efficiency-based ranking framework using uncertain DEA methods, Management Decision, 54(4) 902 – 928.
[27] Liu X., Yuan Z., Xu Y., Jiang S., 2017, Greening cement in China: A cost-effective roadmap, Applied Energy 189, 233–244.
