استفاده از رویکرد تئوری بازی ها و تحلیل پوششی داده ها برای محاسبه کارایی هزینه در شبکه های دو سطحی
الموضوعات :
Raheleh Mousavizadeh
1
,
Mehrzad Navabakhsh
2
,
Ashkan Hafezalkotob
3
1 - Industrial Engineering College, Islamic Azad University, South Tehran Branch, Tehran, Iran,
2 - Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
3 - Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
تاريخ الإرسال : 09 السبت , ذو القعدة, 1442
تاريخ التأكيد : 10 السبت , ربيع الأول, 1443
تاريخ الإصدار : 29 الثلاثاء , جمادى الثانية, 1443
الکلمات المفتاحية:
تئوری بازی ها,
کارایی هزینه,
تحلیل پوششی داده ها,
فرآیند های شبکه ای,
کارایی تکنیکی,
ملخص المقالة :
ارزیابی عملکرد در تحلیل پوششی داده‌ها با استفاده از محاسبه کارایی تکنیکی حاصل می‌شود. اما آنچه که در این نوع محاسبه مد نظر قرار نمی‌گیرد قیمت ورودی‌های مدل است. در محاسبه کارایی تکنیکی مقدار خروجی به ورودی ماکزیمم می‌شود و با استفاده از مدل می‌توان واحد-های تصمیم گیری کارا را مشخص کرد در حالی که ممکن است واحد تصمیم گیری‌ای که در مرز کارایی است و مرجعی برای سایر واحد‌های تصمیم گیری است با صرف هزینه بالا کارا شده باشد و بتوان واحد تصمیم‌گیری‌ای یافت که با هزینه تولید پایینتری در مرز کارایی قرار گرفته باشد. کارایی هزینه به یافتن نقاطی می‌پردازد که با صرف کمترین هزینه بر روی مرز کارایی باشند. با توجه به اهمیت کارایی هزینه و عدم توجه به این مفهوم در ساختارهای شبکه‌ای و چند مرحله‌ای، این پژوهش به بررسی کارایی هزینه در فرآیند دو مرحله‌ای سری محض می‌پردازد. در تحقیق حاضر با بکارگیری مفهوم تئوری بازی‌ها و تحلیل پوششی داده‌ها در حالت متمرکز و غیر متمرکز به ارزیابی عملکرد فرآیندهای دو مرحله‌ای محض می‌پردازیم. به منظور بررسی اعتبار و کارایی مدل‌ها و نیز کاربردی بودن آنها یک مطالعه موردی در صنعت برق ایران مورد استفاده قرار گرفته است و به برخی از نتایج مدیریتی پرداخته می‌شود.
المصادر:
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Abdali E, Fallahnejad R. (2020). A bargaining game model for measuring efficiency of two-stage network DEA with non-discretionary inputs. International Journal of Computer Mathematics: Computer Systems Theory. Jan 2; 5(1): 48-59.
Tavana, M. and K. Khalili-Damghani. (2014). A new two-stage Stackelberg fuzzy data envelopment analysis model. Measurement, 53: 277-296.
Omrani, H., R. Gharizadeh Beiragh, and S. Shafiei Kaleibari. (2015). Performance assessment of Iranian electricity distribution companies by an integrated cooperative game data envelopment analysis principal component analysis approach. International Journal of Electrical Power & Energy Systems, 64: 617-625.
Khalili-Damghani, K. and Z. Shahmir. (2015). Uncertain network data envelopment analysis with undesirable outputs to evaluate the efficiency of electricity power production and distribution processes. Computers & Industrial Engineering, 88: 131-150.
Khalili-Damghani, K., M. Taghavifard, L. Olfat, and K. Feizi. (2011). A hybrid approach based on fuzzy DEA and simulation to measure the efficiency of agility in supply chain: real case of dairy industry. International Journal of Management Science and Engineering Management, 6(3): 163-172.
Despotis, D.K., D. Sotiros, and G. Koronakos. (2016). A network DEA approach for series multistage processes. Omega, 61: 35-48.
Jahangoshai Rezaee, M., H. Izadbakhsh, and S. Yousefi. (2016). An improvement approach based on DEA-game theory for comparison of operational and spatial efficiencies in urban transportation systems. KSCE Journal of Civil Engineering, 20(4): 1526-1531.
Jahangoshai Rezaee, M. and M. Shokry. (2016). Game theory versus multi-objective model for evaluating multi-level structure by using data envelopment analysis. International Journal of Management Science and Engineering Management, 1-11.
Esfandiari, M., A. Hafezalkotob, K. Khalili-Damghani, and M. Amirkhan. (2017). Robust twostage DEA models under discrete uncertain data. International Journal of Management Science and Engineering Management, 12(3): 216-224.
Sadjadi, S.J. and M. Fathollah Bayati. (2016). Two-tier supplier base efficiency evaluation via network DEA: A game theory approach. International Journal of Engineering-Transactions A: Basics, 29(7): 931-939.
Wanke, P., Azad, M. A. K., Emrouznejad, A., & Antunes, J. (2019). A dynamic network DEA model for accounting and financial indicators: A case of efficiency in MENA banking. International Review of Economics & Finance, 61, 52-68.
Fukuyama, H., Matousek, R., & Tzeremes, N. G. (2020). A Nerlovian cost inefficiency two-stage DEA model for modeling banks’ production process: Evidence from the Turkish banking system.Omega, 95, 102198.
Zhu, W., Zhang, Q., & Wang, H. (2019). Fixed costs and shared resources allocation in two-stage network DEA. Annals of Operations Research, 278(1), 177-194.
Chu, J., Wu, J., Chu, C., & Zhang, T. (2020). DEA-based fixed cost allocation in two-stage systems: Leader-follower and satisfaction degree bargaining game approaches. Omega, 94, 102054.
Toloo M, Ertay T. (2014). The most cost efficient automotive vendor with price uncertainty: A new DEA approach. Jun 1; 52: 135-44.
Farrell, M.J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society: Series A (General), 120(3): 253-281.
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Charnes, A., et al. (1986). Two phase data envelopment analysis approaches to policy evaluation and management of army recruiting activities: Tradeoffs between joint services and army advertising. Research report CCS.
Färe, R. and S. Grosskopf. (1997). intertemporal production frontiers: with dynamic DEA. Journal of the Operational Research Society, 48(6): 656-656.
Färe, R., S. Grosskopf, and G. Whittaker. (2007). Network DEA, in Modeling data irregularities and structural complexities in data envelopment analysis. p. 209-240.
Prieto, A.M. and J.L. Zofío. (2007). Network DEA efficiency in input–output models: With an application to OECD countries. European Journal of Operational Research, 178(1): 292-304.
Jaenicke, E.C. (2000). Testing for intermediate outputs in dynamic DEA models: Accounting for soil capital in rotational crop production and productivity measures. Journal of Productivity Analysis, 14(3): 247-266.
Kao, C. (2009). Efficiency decomposition in network data envelopment analysis: A relational model. European Journal of Operational Research, 192(3): 949-962.
Kao, C. (2014). Network data envelopment analysis: A review. European Journal of OperationalResearch, 239(1): 1-16.
Shephard, R.W., D. Gale, and H.W. Kuhn. (1970). Theory of cost and production functions. Princeton University Press Princeton.
Shephard, R.W. and R. Fare. (2013). A Dynamic Theory of Production Correspondences. 1975, DTIC Document. Adler, N., V. Liebert, and E. Yazhemsky, Benchmarking airports from a managerial perspective. Omega, 41(2): 442-458.
Chen, Y., L. Liang, F. Yang, and J. Zhu. (2006). Evaluation of information technology investment: a data envelopment analysis approach. Computers & Operations Research, 33(5): 1368-1379.
Kao C, Hwang SN. (2008). Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan. European journal of operational research. Feb 16; 185(1):418-29.
Liang, L., W.D. Cook, and Zhu., J. (2008). DEA models for two‐stage processes: Game approach and efficiency decomposition. Naval Research Logistics, 55(7): 643-653.
Chen, Y., L. Liang, F. Yang, and J. Zhu. (2006). Evaluation of information technology investment: a data envelopment analysis approach. Computers & Operations Research, 33(5): 1368-1379.
Du, J., L. Liang, Y. Chen, W.D. Cook, and J. Zhu. (2011). A bargaining game model for measuring performance of two-stage network structures. European Journal of Operational Research, 210(2): 390-397.
Mahmoudi R, Emrouznejad A, Rasti-Barzoki M. (2019). A bargaining game model for performance assessment in network DEA considering sub-networks: a real case study in banking. Neural Computing and Applications. Oct; 31(10): 6429-47.
Abdali E, Fallahnejad R. (2020). A bargaining game model for measuring efficiency of two-stage network DEA with non-discretionary inputs. International Journal of Computer Mathematics: Computer Systems Theory. Jan 2; 5(1): 48-59.
Tavana, M. and K. Khalili-Damghani. (2014). A new two-stage Stackelberg fuzzy data envelopment analysis model. Measurement, 53: 277-296.
Omrani, H., R. Gharizadeh Beiragh, and S. Shafiei Kaleibari. (2015). Performance assessment of Iranian electricity distribution companies by an integrated cooperative game data envelopment analysis principal component analysis approach. International Journal of Electrical Power & Energy Systems, 64: 617-625.
Khalili-Damghani, K. and Z. Shahmir. (2015). Uncertain network data envelopment analysis with undesirable outputs to evaluate the efficiency of electricity power production and distribution processes. Computers & Industrial Engineering, 88: 131-150.
Khalili-Damghani, K., M. Taghavifard, L. Olfat, and K. Feizi. (2011). A hybrid approach based on fuzzy DEA and simulation to measure the efficiency of agility in supply chain: real case of dairy industry. International Journal of Management Science and Engineering Management, 6(3): 163-172.
Despotis, D.K., D. Sotiros, and G. Koronakos. (2016). A network DEA approach for series multistage processes. Omega, 61: 35-48.
Jahangoshai Rezaee, M., H. Izadbakhsh, and S. Yousefi. (2016). An improvement approach based on DEA-game theory for comparison of operational and spatial efficiencies in urban transportation systems. KSCE Journal of Civil Engineering, 20(4): 1526-1531.
Jahangoshai Rezaee, M. and M. Shokry. (2016). Game theory versus multi-objective model for evaluating multi-level structure by using data envelopment analysis. International Journal of Management Science and Engineering Management, 1-11.
Esfandiari, M., A. Hafezalkotob, K. Khalili-Damghani, and M. Amirkhan. (2017). Robust twostage DEA models under discrete uncertain data. International Journal of Management Science and Engineering Management, 12(3): 216-224.
Sadjadi, S.J. and M. Fathollah Bayati. (2016). Two-tier supplier base efficiency evaluation via network DEA: A game theory approach. International Journal of Engineering-Transactions A: Basics, 29(7): 931-939.
Wanke, P., Azad, M. A. K., Emrouznejad, A., & Antunes, J. (2019). A dynamic network DEA model for accounting and financial indicators: A case of efficiency in MENA banking. International Review of Economics & Finance, 61, 52-68.
Fukuyama, H., Matousek, R., & Tzeremes, N. G. (2020). A Nerlovian cost inefficiency two-stage DEA model for modeling banks’ production process: Evidence from the Turkish banking system.Omega, 95, 102198.
Zhu, W., Zhang, Q., & Wang, H. (2019). Fixed costs and shared resources allocation in two-stage network DEA. Annals of Operations Research, 278(1), 177-194.
Chu, J., Wu, J., Chu, C., & Zhang, T. (2020). DEA-based fixed cost allocation in two-stage systems: Leader-follower and satisfaction degree bargaining game approaches. Omega, 94, 102054.
Toloo M, Ertay T. (2014). The most cost efficient automotive vendor with price uncertainty: A new DEA approach. Jun 1; 52: 135-44.
Farrell, M.J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society: Series A (General), 120(3): 253-281.
