توسعه مدل تحلیل پوششی داده های شبکه ای باز و ناهمگن
محورهای موضوعی : مهندسی صنایع
وحید اتحادی
1
(دانشجوی دکتری گروه مهندسی صنایع، دانشگاه یزد، یزد، ایران)
حسن حسینی نسب
2
(دانشیار گروه مهندسی صنایع، دانشگاه یزد، یزد، ایران)
محمدباقر فخرزاد
3
(دانشیار گروه مهندسی صنایع، دانشگاه یزد، یزد، ایران)
حسن خادمی زارع
4
(استاد گروه مهندسی صنایع، دانشگاه یزد، یزد، ایران)
کلید واژه: کارآیی, تحلیل پوششی داده ها شبکه ای, ترکیبی ناهمگن,
چکیده مقاله :
در این مقاله، تحلیل پوششی داده ها در ساختار شبکه ای ترکیبی ناهمگن، که خروجی های هر مرحله، می تواند به عنوان ورودی ها، وارد مرحله بعد در همان لایه یا لایه دیگر گردد و یا به عنوان محصولات نهایی از سیستم خارج شود، مورد بحث قرار گرفته است. در این مدل، هر مرحله می تواند، علاوه بر ورودی های میانی، ورودی های مستقل نیز داشته باشد. بدین منظور یک مدل ریاضی توسعه داده شده است که در آن ورودی های مستقل و خروجی های نهایی برای اجزای تشکیل دهنده واحدهای تصمیم گیری، مورد بررسی قرار می گیرد. برای نشان دادن کارایی مدل، داده های واقعی برای 20 واحد تصمیم گیری، مورد استفاده قرار گرفته و نتایج به دست آمده، با نتایج مدل های سنتی مقایسه شده است. نتایج به دست آمده از روش ارائه شده، نقص موجود در روش های سنتی برای تشخیص مناسب واحدهایی که روی مرز کارآ(دارای کارآیی یک) قرار می گیرد را بر طرف می نماید. روش توسعه داده شده می تواند فهم دقیق-تری از عملکرد اجزای واحدهای تصمیم گیری برای مدیران و تصمیم گیران فراهم آورد.
In this paper, data envelopment analysis (DEA) was discussed for non-homogenous mixed network structure where the outputs of each stage can enter the next stage in the same or another layer as inputs or come out from the system as final products. In this model, in addition to intermediate inputs, each stage can also have independent inputs. For this purpose, a mathematical model has been developed in which the independent inputs and final outputs for the components of the decision making units (DMUs) are examined. To show the efficiency of the model, real data for 20 DMUs were used and the results were compared with those of traditional models. The results obtained from the proposed method eliminate the shortcomings of traditional methods for proper detection of units that are on the efficient border (with an efficiency of one). The developed method can provide the managers and decision makers with a more accurate understanding of the performance of components of DMUs.
Adler, N, Liebert, V and Yazhemsky, E. (2013). Benchmarking airports from amanagerial perspective . Omega, Vol. 41. 442-458.
Barat, M, Tohidi, Gh and Sanei, M. (2018). DEA for nonhomogeneous mixed network. Asia Pacific Management Review, Vol. 48. 1-6.
Castelli, L, Pesenti, R and Ukovich, W. (2001). DEA-like models for efficiency evaluation of specialized and interdependent units. European Journal of Operational Research, Vol. 132. 274-286.
Cook, W.D, Zhou, J, Bi, (2010). Network DEA: additive efficiency decomposition European Journal of Operational Research, Vol. 207(2). 1122-1129.
Charnes, A, Cooper, W and Rhodes,(1978). Measuring the efficiency of decision making units. European Journal of Operational Research, Vol. 2(6). 429-444.
Du, J, Chen, Y and Huo, J.(2015). DEA for non-homogeneous parallel networks. Omega, Vol. 56. 122-132.
Ebrahimnejad, A, Tavana, M and Hosseinzadeh Lotfi, F. (2014). A three-stage Dtat Envelopment Analysis model. Measurement, Vol. 49. 308-319.
Fare, R and Grosskopf, (2000) Network DEA. Socio-economic planing science, Vol. 34. 35-49.
Maghbouli, M, Amirteimoori, A and Kordrostami, (2014). Two-stage network structure with undesirable outputs: A DEA based approach. Measurement, Vol. 48. 109-118.
Li, W, Liang, L and Cook, W.D. (2016). DEA Models for Non-Homogeneous DMUs with Different Input. European Journal of Operational Research, Vol. 10. 10-31.
Kao, C and Hwang, (2010). Efficiency measurment for network systems: IT impact on firm performance. Decision Support System, Vol. 48(3). 437-446.
Sarkhosh-Sara, A, Tavassoli, M and Heshmati, A.(2020). Assessing the sustainability of high-, middle-, and low-income countries: A network DEA model in the presence of both zero data and undesirable outputs. Sustainable Production and Consumption, Vol. 20. 42-71.
Strorto, C. (2020). Measuring the efficiency of the urban integrated water service by parallel network. Journal of cleaner production, Vol. 3. 232-250.
Sun, J, Wang, C,(2017). Performance evaluation of heterogeneous bank supply chain systems from the perspective of measurement and decomposition. Computers & Industrial Engineering, Vol. 113. 891-903.
Yang, C.C, Hsia, C.K and Yu, M.M. (2008). Technical efficiency and impact of enviromental regulations in Farrow-to-finish swime production in taiwan. Agriculture Economics, Vol.39. 51-61.
Yu, M.M. (2010). Assessment of airport performance using SBM-NDEA model. Omega, Vol. 38. 440-452.
Zhou, X, Lu, R,(2018). Assessing integrated water use and wastewater treatment systems in China: A mixed network structure two-stage SBM DEA model. Journal of Cleaner Production, Vol. 185. 533-546.
Stefaniec, A, Hosseini, k. (2020). Sustainability assessment of inland transportation in China: A triple bottom line-based network DEA approach. Transportation Research, Vol. 80. 1-20.
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Adler, N, Liebert, V and Yazhemsky, E. (2013). Benchmarking airports from amanagerial perspective . Omega, Vol. 41. 442-458.
Barat, M, Tohidi, Gh and Sanei, M. (2018). DEA for nonhomogeneous mixed network. Asia Pacific Management Review, Vol. 48. 1-6.
Castelli, L, Pesenti, R and Ukovich, W. (2001). DEA-like models for efficiency evaluation of specialized and interdependent units. European Journal of Operational Research, Vol. 132. 274-286.
Cook, W.D, Zhou, J, Bi, (2010). Network DEA: additive efficiency decomposition European Journal of Operational Research, Vol. 207(2). 1122-1129.
Charnes, A, Cooper, W and Rhodes,(1978). Measuring the efficiency of decision making units. European Journal of Operational Research, Vol. 2(6). 429-444.
Du, J, Chen, Y and Huo, J.(2015). DEA for non-homogeneous parallel networks. Omega, Vol. 56. 122-132.
Ebrahimnejad, A, Tavana, M and Hosseinzadeh Lotfi, F. (2014). A three-stage Dtat Envelopment Analysis model. Measurement, Vol. 49. 308-319.
Fare, R and Grosskopf, (2000) Network DEA. Socio-economic planing science, Vol. 34. 35-49.
Maghbouli, M, Amirteimoori, A and Kordrostami, (2014). Two-stage network structure with undesirable outputs: A DEA based approach. Measurement, Vol. 48. 109-118.
Li, W, Liang, L and Cook, W.D. (2016). DEA Models for Non-Homogeneous DMUs with Different Input. European Journal of Operational Research, Vol. 10. 10-31.
Kao, C and Hwang, (2010). Efficiency measurment for network systems: IT impact on firm performance. Decision Support System, Vol. 48(3). 437-446.
Sarkhosh-Sara, A, Tavassoli, M and Heshmati, A.(2020). Assessing the sustainability of high-, middle-, and low-income countries: A network DEA model in the presence of both zero data and undesirable outputs. Sustainable Production and Consumption, Vol. 20. 42-71.
Strorto, C. (2020). Measuring the efficiency of the urban integrated water service by parallel network. Journal of cleaner production, Vol. 3. 232-250.
Sun, J, Wang, C,(2017). Performance evaluation of heterogeneous bank supply chain systems from the perspective of measurement and decomposition. Computers & Industrial Engineering, Vol. 113. 891-903.
Yang, C.C, Hsia, C.K and Yu, M.M. (2008). Technical efficiency and impact of enviromental regulations in Farrow-to-finish swime production in taiwan. Agriculture Economics, Vol.39. 51-61.
Yu, M.M. (2010). Assessment of airport performance using SBM-NDEA model. Omega, Vol. 38. 440-452.
Zhou, X, Lu, R,(2018). Assessing integrated water use and wastewater treatment systems in China: A mixed network structure two-stage SBM DEA model. Journal of Cleaner Production, Vol. 185. 533-546.
Stefaniec, A, Hosseini, k. (2020). Sustainability assessment of inland transportation in China: A triple bottom line-based network DEA approach. Transportation Research, Vol. 80. 1-20.