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Hossein Nakhai, Marzieh, Rostamy Malkhalifeh, Mohsen, Hosseinzadeh Lotfi, Farhad. (1405). A Fuzzy Additive DEA Framework for Conditional Short-Run Scale-Direction Assessment with Quasi-Fixed Inputs and Minimum Service-Level Constraints. فصلنامه علمی زیست فناوری گیاهان زراعی, (), -. doi: 10.30473/coam.2026.79995.1484
Marzieh Hossein Nakhai; Mohsen Rostamy Malkhalifeh; Farhad Hosseinzadeh Lotfi. "A Fuzzy Additive DEA Framework for Conditional Short-Run Scale-Direction Assessment with Quasi-Fixed Inputs and Minimum Service-Level Constraints". فصلنامه علمی زیست فناوری گیاهان زراعی, , , 1405, -. doi: 10.30473/coam.2026.79995.1484
Hossein Nakhai, Marzieh, Rostamy Malkhalifeh, Mohsen, Hosseinzadeh Lotfi, Farhad. (1405). 'A Fuzzy Additive DEA Framework for Conditional Short-Run Scale-Direction Assessment with Quasi-Fixed Inputs and Minimum Service-Level Constraints', فصلنامه علمی زیست فناوری گیاهان زراعی, (), pp. -. doi: 10.30473/coam.2026.79995.1484
Hossein Nakhai, Marzieh, Rostamy Malkhalifeh, Mohsen, Hosseinzadeh Lotfi, Farhad. A Fuzzy Additive DEA Framework for Conditional Short-Run Scale-Direction Assessment with Quasi-Fixed Inputs and Minimum Service-Level Constraints. فصلنامه علمی زیست فناوری گیاهان زراعی, 1405; (): -. doi: 10.30473/coam.2026.79995.1484

A Fuzzy Additive DEA Framework for Conditional Short-Run Scale-Direction Assessment with Quasi-Fixed Inputs and Minimum Service-Level Constraints

Control and Optimization in Applied Mathematics
مقالات آماده انتشار، اصلاح شده برای چاپ، انتشار آنلاین از تاریخ 19 آبان 1405    اصل مقاله (693.66 K)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.30473/coam.2026.79995.1484
نویسندگان
Marzieh Hossein Nakhai؛ Mohsen Rostamy Malkhalifeh* ؛ Farhad Hosseinzadeh Lotfi
Department of Mathematics, SR.C., Islamic Azad University, Tehran, Iran
چکیده
A fuzzy additive data envelopment analysis (DEA) framework is developed to evaluate the scale direction in the short run when part of the capacity is quasi-fixed and a minimum service level is required. A common-realization triangular possibility method is formulated as two sign-dependent linear programs, and the scale parameter is studied over the optimal face to reduce dependence on an arbitrary solver-selected optimal solution. The framework is applied to 2024 National Transit Database (NTD) data for 284 directly operated motor-bus agencies. The additive model, the capacity-restricted model, the deterministic capacity- and service-constrained model, and the baseline fuzzy model identify 8, 16, 34, and 28 agencies on the frontier, respectively. The baseline fuzzy frontier includes 15 agencies with an increasing direction, 11 with no improvement, and 2 with a decreasing direction, according to the specified LP rule. Fleet size and the number of revenue hours are strongly correlated (Pearson r = 0.9909). A 21-point service-floor sensitivity analysis, an independent LP implementation, alternative treatments of uncertainty, and stratified-sample models show that the results depend strongly on the assumed technology and on the uncertainty assumptions. In particular, scaling the service floor with the scale parameter changes the classification of the baseline frontier to 3, 5, and 20 agencies in the increasing, zero-improvement, and decreasing categories, respectively. The mathematical results establish feasibility and boundedness, the exactness of the two-sign-branch decomposition, and the identifiability of the effective fuzzy spread εf = δ(1 - η). The scale-direction classifications describe the specified global constrained program and should not be interpreted as estimates of local production elasticity.

تازه های تحقیق
  • A conditional fuzzy additive DEA model assesses short-run scale direction with quasi-fixed fleet capacity
  • A minimum service-level constraint keeps benchmarks from gaining efficiency by cutting service
  • Two sign-dependent linear programs give an exact formulation of the common-realization fuzzy model
  • Optimal-face scale intervals make direction labels independent of the solver’s choice of optimum
  • Applied to 284 U.S. bus agencies, with sensitivity analyses of service floor, fuzzy spread, and thresholds
کلیدواژه‌ها
Fuzzy data envelopment analysis؛ Additive optimization؛ Conditional scale direction؛ Quasi-fixed input؛ Minimum service level؛ Possibility theory
مراجع

[1] Amirteimoori, A., Allahviranloo, T., Fakhr Mousavi, S. M. (2025). “Returns-to-scale and scale economies of two-stage production processes using a fully fuzzy range-adjusted measure model with strong complementary slackness conditions”. Expert Systems with Applications, 262, 125606. https://doi.org/10.1016/j.eswa.2024.125606

[2] Banker, R. D., Charnes, A., Cooper, W. W. (1984). “Some models for estimating technical and scale inefficiencies in data envelopment analysis”. Management Science, 30(9), 1078– 1092. https://doi.org/10.1287/mnsc.30.9.1078

[3] Banker, R. D., Morey, R. C. (1986). “Efficiency analysis for exogenously fixed inputs and outputs”. Operations Research, 34(4), 513–521. https://doi.org/10.1287/ opre.34.4.513

[4] Banker, R. D., Thrall, R. M. (1992). “Estimation of returns to scale using data envelopment analysis”. European Journal of Operational Research, 62(1), 74–84. https://doi.org/10.1016/0377-2217(92)90178-C

[5] Blavet, T., Ouellette, P., Vigeant, S. (2023). “α-returns to scale with quasi-fixed inputs: An application to Québec hospitals”. Applied Economics, 55(58), 6922–6938. https://doi.org/10.1080/00036846.2023.2166664

[6] Charnes, A., Cooper, W. W., Rhodes, E. (1978). “Measuring the efficiency of decision making units”. European Journal of Operational Research, 2(6), 429–444. https://doi. org/10.1016/0377-2217(78)90138-8

[7] Chu, X., Fielding, G. J., Lamar, B. W. (1992). “Measuring transit performance using data envelopment analysis”. Transportation Research Part A: Policy and Practice, 26(3), 223– 230. https://doi.org/10.1016/0965-8564(92)90033-4

[8] Cooper, W. W., Park, K. S., Pastor, J. T. (1999). “RAM: A range adjusted measure of inefficiency for use with additive models, and relations to other models and measures in DEA”. Journal of Productivity Analysis, 11(1), 5–42. https://doi.org/10.1023/A:1007701304281

[9] Daraio, C., Diana, M., Di Costa, F., Leporelli, C., Matteucci, G., Nastasi, A. (2016). “Efficiency and effectiveness in the urban public transport sector: a critical review with directions for future research”. European Journal of Operational Research, 248(1), 1–20. https://doi.org/10.1016/j.ejor.2015.05.059

[10] Dyson, R. G., Allen, R., Camanho, A. S., Podinovski, V. V., Sarrico, C. S., Shale, E. A. (2001). “Pitfalls and protocols in DEA”. European Journal of Operational Research, 132(2), 245–259. https://doi.org/10.1016/S0377-2217(00)00149-1

[11] Emami, F., Nasirzade Tabrizi, T. (2016). “An additive model for estimation return to scale in regulated environment with quasi-fixed inputs in data envelopment analysis (DEA)”. International Journal of Data Envelopment Analysis, 4(1), 893–901. https://sanad. iau.ir/ar/Article/826433

[12] Emami, F., Rostamy Malkhalifeh, M., Safdari, H. (2014). “Estimation of returns to scale in the presence of undesirable (bad) outputs in DEA when the firm is regulated”. International Journal of Data Envelopment Analysis, 2(4), 487–495. http://ijdea.srbiau. ac.ir

[13] Federal Transit Administration (2024). “TS2.1: Service data and operating expenses time series by mode” [Data set]. National Transit Database, U.S. Department of Transportation. Retrieved November 5, 2026, from https://www.transit.dot.gov/ntd/data-product/ts21-service-data-and-operating-expenses-time-series-mode-2

[14] Hatami-Marbini, A., Emrouznejad, A., Tavana, M. (2011). “A taxonomy and review of the fuzzy data envelopment analysis literature: two decades in the making”. European Journal of Operational Research, 214(3), 457–472. https://doi.org/10.1016/j.ejor. 2011.02.001

[15] Huangfu, Q., Hall, J. A. J. (2018). “Parallelizing the dual revised simplex method”. Mathematical Programming Computation, 10(1), 119–142. https://doi.org/10.1007/ s12532-017-0130-5

[16] Khodabakhshi, M., Gholami, Y., Kheirollahi, H. (2010). “An additive model approach for estimating returns to scale in imprecise data envelopment analysis”. Applied Mathematical Modelling, 34(5), 1247–1257. https://doi.org/10.1016/j.apm.2009.08.011

[17] Lertworasirikul, S., Fang, S. C., Joines, J. A., Nuttle, H. L. W. (2003). “Fuzzy data envelopment analysis (DEA): A possibility approach”. Fuzzy Sets and Systems, 139(2), 379–394. https://doi.org/10.1016/S0165-0114(02)00484-0

[18] Noori, Z., Zhiani Rezai, H., Davoodi, A., Kordrostami, S. (2022). “Finding the most efficient DMU in DEA: A model-free procedure”. Control and Optimization in Applied Mathematics, 7(1), 15–29. https://doi.org/10.30473/coam.2022.62871.1193

[19] Odeck, J., Alkadi, A. (2001). “Evaluating efficiency in the Norwegian bus industry using data envelopment analysis”. Transportation, 28(3), 211–232. https://doi.org/10. 1023/A:1010333518966

[20] Ouellette, P., Quesnel, J. P., Vigeant, S. (2012). “Measuring returns to scale in DEA models when the firm is regulated”. European Journal of Operational Research, 220(2), 571–576. https://doi.org/10.1016/j.ejor.2012.01.048

[21] Payan, A., Hajihosseini, A., Nosrat, A. (2022). “Detection of the type of left and right returns-to-scales using envelopment DEA models in crisp and fuzzy environments: an application for predicting changes in the stock market”. International Journal of Computational Intelligence Systems, 15(1), Article 26. https://doi.org/10.1007/ s44196-022-00079-4

[22] Seiford, L. M., Zhu, J. (1999). “An investigation of returns to scale in data envelopment analysis”. Omega, 27(1), 1–11. https://doi.org/10.1016/S0305-0483(98) 00025-5

[23] Simar, L., Wilson, P. W. (1998). “Sensitivity analysis of efficiency scores: How to bootstrap in nonparametric frontier models”. Management Science, 44(1), 49–61. https://doi.org/10.1287/mnsc.44.1.49

[24] Sueyoshi, T., Sekitani, K. (2005). “Returns to scale in dynamic DEA”. European Journal of Operational Research, 161(2), 536–544. https://doi.org/10.1016/j.ejor. 2003.08.055

[25] Yu, M. M. (2008). “Measuring the efficiency and return to scale status of multi-mode bus transit: evidence from Taiwan’s bus system”. Applied Economics Letters, 15(8), 647–653. https://doi.org/10.1080/13504850600721858

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