پیوندهای مفید
آمار
| تعداد نشریات | 21 |
| تعداد شمارهها | 689 |
| تعداد مقالات | 10,025 |
| تعداد مشاهده مقاله | 71,034,081 |
| تعداد دریافت فایل اصل مقاله | 62,481,499 |
Proposing an extended model of dynamic data envelopment analysis using goal programming to calculate relative efficiency of industrial development in provinces of Iran | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 115، دوره 13، شماره 2، مهر 2022، صفحه 1407-1418 اصل مقاله (3.54 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.26100.3229 | ||
| نویسندگان | ||
| Abolfazl Sadeghian* ؛ Hossein Safari؛ Nima Grossi Mokhtarzadeh؛ Aliyeh Kazemi | ||
| Industrial Management Department, Faculty of Management, University of Tehran, Iran | ||
| تاریخ دریافت: 07 آبان 1400، تاریخ بازنگری: 08 آذر 1400، تاریخ پذیرش: 18 دی 1400 | ||
| چکیده | ||
| The purpose of the present study was to provide a dynamic model of data envelopment analysis by utilizing from goal programming based on variables of population and education in order to evaluate the relative efficiency of industrial development in provinces of Iran during the years 2007 to 2016. For this purpose, the demographic, education and industry development variables were firstly determined with the help of 42 university and industry experts, then the research model was developed which included: objective function in the form of minimizing adverse deviations of goal constraints based on variations of units at different time periods, and model constraints in the forms of goal constraints and system constraints. In the next step, the model was solved through GAMS Software after designing and implementing dynamic and goal models of data envelopment analysis for provinces of the country in the mentioned period. The relative efficiency of industries development of the provinces was separately calculated for each of the understudy years, then the obtained values were used to calculate the relative efficiency of industry development for each province. According to results, Khuzestan province was ranked first and Sistan and Baluchistan province ranked last in term of average relative efficiency. | ||
| کلیدواژهها | ||
| industries development؛ population؛ education؛ data envelopment analysis؛ dynamic data؛ goal programming | ||
| مراجع | ||
|
[1] A. Azar, M.A. Zarei, Mohammadi, A. Moghbel Baarz and A. Khadivar, Measuring the efficiency of bank branches with grid data envelopment analysis approach (one of the banks of Guilan province), Quart. J. Monetary-Bank. Res. 7 (2014), no. 20, 285–305. [2] M. Bastani, S. Ketabi and M. Ghandehari, Providing an integrated model for product allocation to distributors in the supply chain using data envelopment analysis and ideal planning, a case study of the automotive industry, Oper. Res. Appl. 11 (2014), no. 40, 119–131.[3] M. Bell and M. Alb, Knowledge systems and technological dynamism in industrial cluster in developing countries, World Dev. 27 (2013), no. 9, 1715–1734. [4] L.M. Drake and R. Simper, The economics of managerialism and the drive for efficiency in policing, Manag. Decision Econ. 25 (2013), 509–523. [5] A. Emrouznejad and G.L. Yang, A survey and analysis of the first 40 years of scholarly literature in DEA: 1978-2016, Socio-Econ. Plann. Sci. 61 (2018), 4–8. [6] C. Guo, R. Abbasi Shureshjani, A.A. Foroughi and J. Zhu, Decomposition weights and overall efficiency in twostage additive network DEA, Eur. J. Oper. Res. 257 (2017), no. 3, 896–906. [7] A. Haghieghat Talab, Familiarity with Central Banks of the World, Monetary, and Banking Research Institute, 2015. [8] O. Herrera-Restrepoa, K. Triantisa, J. Trainorb and P. Murray-Tuitec, A multi-perspective dynamic network performance efficiency measurement of an evacuation: a dynamic network-DEA approach, Omega 60 (2016), 45—59. [9] E. Kharat Zebardast and P. Moazeddin, Measuring the Industrial Development of the Regions of the Country, Tehran: Center for Urban Planning and Architecture Studies and Research, Department of Economic Studies, 1992. [10] M. Khoveyni, R. Eslami and G.-L. Yang, Negative data in DEA: recognizing congestion and specifying the least and the most congested decision-making units, Comput. Oper. Res. 79 (2016), 39–48. [11] M.R. Mehregan, Quantitative Models in Organizational Performance Evaluation (Data Envelopment Analysis),[12] L. Olfat, M. Amiri, J. Bamdad Soufi, M. Pishda, A dynamic network efficiency measurement of airports performance considering sustainable development concept: a fuzzy dynamic network-DEA approach, J, Air Transport Manag, 57 (2016), 272–290. [13] H. Omrani and S.K. Shafaat, Presentation of a model based on data envelopment analysis and game theory for ranking units, Int. Conf. Ind. Engin. Manag. 2016.[14] H. Omrani and E. Soltanzadeh, Dynamic DEA models with network structure: an application for Iranian airlines, J. Air Transport Manag. 57 (2016), 52–61. [15] F.A.S. Piran, D.P. Lacerda, L.F.R. Camargo, C.F. Viero, A. Dresch and P.A. Cauchick-Miguel, Product modularization and effects on efficiency: an analysis of a bus manufacturer using data envelopment analysis (DEA), Int. J. Prod. Econ. 182 (2016), 1–13. [16] S.M. Razavi, S. Shahriari and M. Ahmadpor Dariani, Evaluation of innovative performance of knowledge based company by network data envelopment analysis-game theory approach, Ind. Manag. J. 7 (2015), no. 4, 721–742. [17] F. Roozbeh, R. Eslami and N. Ahadzadeh, Estimating most productive scale size with double frontiers in data envelopment analysis using negative data, Int. J. Data Env. Anal. 3 (2016), no. 4. [18] I. Shah Tahmasbi, H. Taheri and S. Sham Ealahi, Evaluation of the relative efficiency of provinces in the economic indicators of culture during the third and fourth development plans with the data envelopment analysis approach, Culture Strategy J. 6 (2013), no. 24, 163–183. [19] A. Tavana and M. Salehi Sarbizhan, A new model for suppliers ranking using grayscale theory and data envelopment analysis based on uncertainty, Int. Conf. Ind. Engin. Manag. 2016. [20] M. Tavana, H. Shabanpour, S. Yousefi and R. Farzipoor Saen, A hybrid goal programming and dynamic data envelopment analysis framework for sustainable supplier evaluation, Neural Comput. Appl. 28 (2016), no. 12, 3683–3696. | ||
|
آمار تعداد مشاهده مقاله: 44,542 تعداد دریافت فایل اصل مقاله: 51,000 |
||