1. Özsoy, V.S., Belgin, Ö., Balkan, D. (2022).
A Novel approach for determining common weights in two division network DEA: a case study of Science and Technology Parks in Turkey. Technology Analysis & Strategic Management, 34(10), 1124-1138.
Bansal, P., Srivastava, S., & Aggarwal, A. (2024). The efficiency analysis of two-stage dynamic interval DEA model incorporating cumulative prospect theory: an application to Indian airlines. Soft Computing., xx(xx), xx-xx. (indexed in SCI).
Yılık, M.A., Kondakçı, Y. (2024). Technology Development Zones as a Form of University–Industry Relations: A Multiple-Case Study. Higher Education Policy, xx(xx), xx-xx. (indexed in SSCI).
Babaei, A., Khedmati, M., & Jokar, M. R. A. (2023). A novel algorithm for evaluating the configurations of omni-channel distribution network considering transparency and consensus formation. International Journal of Shipping and Transport Logistics, 16(1-2), 170-193. (indexed in SSCI).
Jiang, W., Liu, S. and Li, S. (2023). An extended cross-efficiency evaluation method based on information entropy with an application to the urban logistics industry. Journal of Modelling in Management, 18(2), 578-601. (indexed in ESCI).
2. Muvingi, J., Peer, A.A.I., Lotfi, F.H., Özsoy, V.S. (2022).
Hierarchical Groups DEA Cross-efficiency and TOPSIS technique: An application on mobile money agents locations. International Journal of Mathematics in Operational Research, 21 (2), 171-199.
Muvingi, J., Peer, A.A.I., Jablonský, J., Azizi, H., Lotfi, F.H. (2024). Hierarchical fuzzy DEA model with double frontiers combined with TOPSIS technique: application on mobile money agents locations. OPSEARCH. doi: 10.1007/s12597-023-00734-0. (indexed in ESCI).
Muvingi, J., Peer, A.A.I., Jablonsky, J., Lotfi, F.H. (2023). Hierarchical groups DEA super-efficiency and group TOPSIS technique: Application on mobile money agents locations,Expert Systems With Applications, 234, 121033. doi: 10.1016/j.eswa.2023.121033. (indexed in SCI-Exp.).
3. Doğan, M.İ., Özsoy, V.S., & Örkcü, H.H. (2021).
Performance Management of OECD Countries on Covid-19 Pandemic: A Criticism using Data Envelopment Analysis Models. Journal of Facilities Management, 19(4), 479-499.
Singh, S. A., & Singh, E. B. (2023). Technical Efficiency of North Eastern Region of India Health Systems in Combating COVID-19. Indian Journal of Science and Technology, 16(38), 3218-3222. doi: 10.17485/IJST/v16i38.1639
Selamzade, F., Ersoy, Y., Ozdemir, Y., & Celik, M. Y. (2023). Health Efficiency Measurement of OECD Countries Against the COVID-19 Pandemic by Using DEA and MCDM Methods. Arabian Journal for Science and Engineering, 1-18. doi: 10.1007/s13369-023-08114-y (indexed in SCI-Exp.)
Dichi-Romero, M. A., García-Cortés, L. R., & Ramos-Valle, D. (2023). Hospital Management of the Eastern Mexico Regional Administrative Operating Body in the Sars-Cov-2 Pandemic: Technical Efficiency. J Med-Clin Res & Rev, 7(1), 1-7.
Pan, J., Fan, R., Zhang, H., Gao, Y., Shu, Z., & Chen, Z. (2022). Investigating the Effectiveness of Government Public Health Systems against COVID-19 by Hybrid MCDM Approaches. Mathematics, 10(15), 1-20. doi: 10.3390/math10152678. (indexed in SCI-Exp.).
Taherinezhad, A. & Alinezhad, A. (2022). Nations performance evaluation during SARS-CoV-2 outbreak handling via data envelopment analysis and machine learning methods. International Journal of Systems Science: Operations & Logistics. xx(xx), xx-xx. doi: 10.1080/23302674.2021.2022243. (indexed in SCI-Exp.).
4. Özsoy, V.S., & Örkcü, H.H. (2021).
Structural and operational management of Turkish airports: a bootstrap data envelopment analysis of efficiency.
Utilities Policy, 69, 101180.
Güner, S., Antunes, J. J. M., Codal, K. S., & Wanke, P. (2024). Network centrality driven airport efficiency: A weight-restricted network DEA. Journal of Air Transport Management, 116, 102551. doi: 10.1016/j.jairtraman.2024.102551. (indexed in SSCI).
Khuznuzzan, A., & Widagdo, D. (2024). Implementasi Peraturan Menteri Perhubungan Nomor 95 Tahun 2021 mengenai Pelatihan Penanggulangan Keadaan Darurat di Bandar Udara Internasional Zainuddin Abdul Madjid Lombok . Indonesian Journal of Aviation Science and Engineering, 1(1), 9. doi: 10.47134/pjase.v1i1.2227.
Zhao, L., Cheng, L., Zhou, C., Ding, L., Wang, F. (2024). 3D conditional random fields simulation for rockfill compaction quality assessment with sparse EVD measurement. Results in Engineering, 101706. doi: 10.1016/j.rineng.2023.101710. (indexed in ESCI).
Kurt, Ş., Yüksel, M. K., & Dinçergök, B. (2023). Data driven approach for weight restricted data envelopment analysis models with single output. Journal of Turkish Operations Management, 7(2), 1768 - 1779. doi: 10.56554/jtom.1333333.
Omrani, H., Yang, Z., Karbasian, A., & Teplova, T. (2023). Combination of top-down and bottom-up DEA models using PCA: A two-stage network DEA with shared input and undesirable output for evaluation of the road transport sector. Socio-Economic Planning Sciences, 101706. doi: 10.1016/j.seps.2023.101706. (indexed in SSCI, SCI-Exp.).
Eren, M. & Doğan, M. A. (2023). Measurement of Process-Performances of Turkish Airports Using Network Data Envelopment Analysis, Journal of Aviation, 7 (2), 272-283. doi: 10.30518/jav.1296416.
Nzioka, C. M., & Mwaura, P. (2023). Customer Strategic Orientation and Organizational Performance of Kenya Airports Authority, The International Journal of Business Management and Technology, 7(2), 329-335.
Cifuentes-Faura, J., Faura-Martínez, U. (2023). Measuring Spanish airport performance: A bootstrap data envelopment analysis of efficiency. Utilities Policy, 80, 101457. doi: 10.1016/j.jup.2022.101457. (indexed in SSCI, SCI-Exp.).
Thomas, N., & Jha, K. N. (2022). Structural efficiency assessment of regional airports: Lessons from India. Utilities Policy, 79, 101449. doi: 10.1016/j.jup.2022.101449. (indexed in SSCI).
Güner, S., Antunes, J., Seçkin Codal, K., Wanke, P. (preprint). Network Centrality and Efficiency in Turkish Airports: A Hybrid Network DEA. doi: 10.2139/ssrn.4252267.
Montoya-Quintero, D.M., Larrea-Serna, O.L., Jiménez-Builes, J.A. (2022). Evaluation of the Efficiency of Regional Airports Using Data Envelopment Analysis. Informatics, 9, 90. doi: 10.3390/informatics9040090. (indexed in eSCI).
Mohamad Razi, N.F., Baharun, N., Omar, N. (2022). Machine Learning Predictive Model of Academic Achievement Efficiency based on Data Envelopment Analysis. Mathematical Sciences and Informatics Journal, 3(1), 86-99. doi: 10.24191/mij.v3i1.18284.
Ledyaev A., Kavkazskiy V., Davidenko E. (2022) Examination of the Stress-Strain State of Service Tunnels at the Airport “Domodedovo”. In: Manakov A., Edigarian A. (eds) International Scientific Siberian Transport Forum TransSiberia - 2021. TransSiberia 2021. Lecture Notes in Networks and Systems, vol 402. Springer, Cham. doi: 10.1007/978-3-030-96380-4_4.
Güner, S., & Seckin Codal, K. (2022). Endogenous and exogenous sources of efficiency in the management of Turkish airports. Utilities Policy, 76, 101370. doi: 10.1016/j.jup.2022.101370. (indexed in SSCI).
Yılmaz, M.K., Kusakci, A.O., Aksoy, M., Hacioglu, u. (2022). The evaluation of operational efficiencies of Turkish airports: An integrated spherical fuzzy AHP/DEA approach. Applied Soft Computing, 108620. doi: 10.1016/j.asoc.2022.108620. (indexed in SCI-Exp.).
Szaruga, E., & Załoga, E. (2022). Sustainable Development Programming of Airports by Identification of Non-Efficient Units. Energies, 15(3), 932. doi: 10.3390/en15030932. (indexed in SCI-Exp.).
Dellnitz, A. (2022). Big data efficiency analysis: Improved algorithms for data envelopment analysis involving large datasets. Computers & Operations Research. 137, 105553. doi: 10.1016/j.cor.2021.105553. (indexed in SCI-Exp.).
Gov, S. A. (2022). SWOT Analysis and a Case Study at Kayseri Airport. In J. Santos (Ed.), Cases on Digital Strategies and Management Issues in Modern Organizations, edited by José Duarte Santos, IGI Global, 236-254. doi: 10.4018/978-1-7998-1630-0.ch010
Kaur, S., Kumar, J.D., Chaudhary, G. (2021) An innovative multi-criteria decision-making framework for assessing India's airport operating efficiency, Fusion: Practice and Applications, 4(2), 72-85. doi: 10.54216/FPA.040204.
Kiracı, K. & Yalçın, S. (2021). Dünyadaki Düşük Maliyetli Havalimanlarının Performanslarının Veri Zarflama Analiziyle Değerlendirilmesi. Erciyes University Journal of Faculty of Economics and Administrative Sciences. 60, 499-517. doi: 10.18070/erciyesiibd.907439
Lee, H., Choi, Y., Yang, F., & Debbarma, J. (2021). The governance of airports in the sustainable local economic development. Sustainable Cities and Society, 74, 103235. doi: 10.1016/j.scs.2021.103235. (indexed in SCI-Exp.).
Güner, S., & Cebeci, H. İ. (2021). Multi-Period Efficiency Analysis of Major European and Asian Airports. Transport Policy, 107, 24-42. doi: 10.1016/j.tranpol.2021.04.015. (indexed in SSCI).
5. Özsoy, V.S., Örkcü, M., & Örkcü, H.H. (2021).
A minimax approach for selecting the overall and stage-level most efficient unit in two stage production processes.
Annals of Operations Research, 300, 137-169.
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. doi: 10.30473/COAM.2022.62871.1193.
Pirlot, M. Opinion Makers Section. Management Science, 54(1), 56-70.
6. Özsoy, V.S. (2021). The determination of the most suitable inertia weight strategy for particle swarm optimization via the minimax mixed-integer linear programming model.
Engineering Computations, 38(4), 1933-1954.
Al‐Gabalawy, M. (2021). Advanced machine learning tools based on energy management and economic performance analysis of a microgrid connected to the utility grid. International Journal of Energy Research, xx(xx), xx-xx. doi: 10.1002/er.6764. (indexed in SCI-Exp.).
7. Özsoy, V.S., Örkcü, H.H., & Örkcü, M. (2021). A simplistic approach without epsilon to choose the most efficient unit in data envelopment analysis.
Expert Systems with Applications, 168, 114472.
Kiani Mavi, R., Kiani Mavi, N., Hosseini Shekarabi, S.A. et al. (2024). Supply Chain Resilience: A Common Weights Efficiency Analysis with Non-discretionary and Non-controllable Inputs. Global Journal of Flexible Systems Management, xx,xx-xx. doi: 10.1007/s40171-024-00380-5.
Ucar, E., & Karsak, E.E. (2023). Evaluating Educational Performance of OECD Countries with Common-Weight DEA-Based Models. Journal of the Knowledge Economy, xx(xx), xx-xx. doi: 10.1007/s13132-023-01619-9. (indexed in SSCI)
Mavi, R. K., Mavi, N. K., Saen, R. F., & Goh, M. (2022). Common weights analysis of renewable energy efficiency of OECD countries. Technological Forecasting and Social Change, 185, 122072. doi: 10.1016/j.techfore.2022.122072. (indexed in SSCI)
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. doi: 10.30473/COAM.2022.62871.1193.
8. Küçük, A., Demirci, M., Kerman, G., Özsoy, V.S. (2021).
Evaluating of Hospital Appointment Systems in Turkey: Challenges and Opportunities.
Health Policy and Technology, 10(1), 69-74.
Sevik I, Ciceklioglu M. Healthcare Access Worsened for Women in Precarious Housing During the COVID-19 Pandemic: A Qualitative Study. INQUIRY: The Journal of Health Care Organization, Provision, and Financing, 61. doi: 10.1177/00469580241246478 (indexed in SSCI, SCI-Exp.)
Khalil, L., and Serhier, Z., (2023). Exploring user acceptance of medical e-appointment systems for mental healthcare: A systematic literature review, SHS Web Conf., 175, 01018. doi: 10.1051/shsconf/202317501018
Özen, O. , Köse, İ. , Yıgıt, P. , Güner, Ş. & Aydın, S. (2023). Analysis of Density and Patient Wait Times in Terms of System Management in Turkish Hospital: Setting A Pattern by Days and Hours of The Week, Journal of Health Systems and Policies, xx, xx - xx. doi: 10.52675/jhesp.1316821
Wijaya, A., Sarma Sangkot, H., & Sri Dewi Hastuti Suryandari, E. (2023). Prototyping an Online Patient Registration Based on a Smartphone App at the Malang Primary Health Care. KnE Medicine, 3(3), 329–341. doi: 10.18502/kme.v3i3.13519
Güç, G. , Gedik, M. , Çılgın, C. & Tuncalı Yaman, T. (2023). Process Management and Improvement in Health Services: A Hospital Appointment System Example. Journal of Health Systems and Policies, 5(1), 11-40. doi: 10.52675/jhesp.1257436
Solmaz, H., Uludağ, B. (2023). Comparison of patients’ admissions to the cardiology outpatient clinics between the appointment system and the queue system. Turk Kardiyol Dern Ars. 51(3):188-195. doi: 10.5543/tkda.2023.84343 (indexed in eSCI)
Primadhani S.W., Ilyas, Y. Atthahirah, A.I. (2023). Online Registration System as Hospital’s Service Improvement Strategy: Literature Review.Media Publikasi Promosi Kesehatan Indonesia (MPPKI), 6(1), 20-26. doi: 10.56338/mppki.v6i1.2890
Tunder, N.(2022). Reasons Why Patients Not Going To Outpatient Clinic Appointments and Their Determinants: Example of a University Hospital, [Master’s Thesis, Hacettepe University]. link
Lieneck C, Ramamonjiarivelo Z, Cox J, Dominguez J, Gersbach K, Heredia E, Khan A. (2021). Patient Throughput Initiatives in Ambulatory Care Organizations during the COVID-19 Pandemic: A Systematic Review. Healthcare. 9(11):1474. doi: 10.3390/healthcare9111474 (indexed in SSCI,SCIE)
9. Özsoy, V.S., Ünsal, M.G. & Örkcü, H.H. (2020).
Use of the heuristic optimization in the parameter estimation of generalized gamma distribution: comparison of GA, DE, PSO and SA methods.
Computational Statistics, 35, 1895–1925.
Javadi, A., Haghighi, H., Pornpipatsakul, K., Chaichaowarat, R. (2024). Data-Driven Position and Stiffness Control of Antagonistic Variable Stiffness Actuator Using Nonlinear Hammerstein Models. Journal of Sensor and Actuator Networks, 13(2):29. doi: 10.3390/jsan13020029. (indexed in ESCI).
Yang, Z., Li, Y., Liu, X., Li, Y., Cui, P., Zheng, S., & Zhou, X. (2024). Improving the Uniformity of the Residual Magnetic Field in the MSR Using Independent Coils Compensation Method. IEEE Transactions on Automation Science and Engineering. doi: 10.1109/TASE.2024.3358542. (indexed in SCI-Exp.).
Jabbar, N. A. A., & Kalaf, B. A. (2024). A Novel Optimization Algorithm for Estimating the Parameters of the Truncated Distribution Depending on Survival Function. Pak. J. Statist, 40 (1), 105-122.
Sayed, A.I.A., Sabri, S.R.M. (2023). Generalized gamma distribution based on the Bayesian approach with application to investment modelling, International Journal for Simulation and Multidisciplinary Design Optimization, 14. doi: 10.1051/smdo/2023011
Sayed, A.I.A., Sabri, S.R.M. (2023). On Estimating the Parameters of the Generalised Gamma Distribution based on the Modified Internal Rate of Return for Long-Term Investment Strategy, Pertanika Journal of Science & Technology, 31(5), 2241 - 2255. doi: 10.47836/pjst.31.5.07
Chy, T.J. (2023). Genetic Algorithm for Determining Optimal Unit Hydrograph for Harpeth River Watershed, Tennessee, Computational Research Progress in Applied Science & Engineering, CRPASE: Transactions of Civil and Environmental Engineering, 9, 1–5, Article ID: 2836. doi: 10.52547/crpase.9.1.2836
Wang, X., Yao, Y., Yu, Y., Niu, P., & Yang, H. (2023). Statistical image watermark decoder by modeling local RDWT difference domain singular values with bivariate weighted Weibull distribution. Applied Intelligence, 53, 96–120. doi: 10.1007/s10489-022-03536-x. (indexed in SCI-Exp.).
Abdeljalil, D., Benhadj, N., Kolsi, H., Krichen, M., Neji, R. (2022). Application of PSO in the Design of Permanent Magnet Synchronous Generator for 1.5MW wind turbine, 2022 IEEE 21st international Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA), Sousse, Tunisia, 2022, pp. 533-538, doi: 10.1109/STA56120.2022.10019007
Abdelwanis, M. I. (2022). Linear Induction Motor Parameter Estimation Based on Gray Wolves Optimization Algorithm, 2022 23rd International Middle East Power Systems Conference (MEPCON), Cairo, Egypt, 2022, pp. 1-6, doi: 10.1109/MEPCON55441.2022.10021695
Taketomi, N., Yamamoto, K., Chesneau, C., Emura, T. (2022). Parametric Distributions for Survival and Reliability Analyses, a Review and Historical Sketch. Mathematics, 10(20), 3907. doi: 10.3390/math10203907. (indexed in SCI-Exp.).
Lin, J., Pan, L. (2022). Multiobjective trajectory optimization with a cutting and padding encoding strategy for single-UAV-assisted mobile edge computing system. Swarm and Evolutionary Computation, 75, 101163. doi: 10.1016/j.swevo.2022.101163. (indexed in SCI-Exp.).
Noinang, S.Sabir, Z., Altamirano, G. C., Zahoor Raja, M. A., Sànchez-Chero M. J. et al. (2022). Swarming computational techniques for the influenza disease system, Computers, Materials & Continua, 73(3), 4851–4868. doi: 10.32604/cmc.2022.029437. (indexed in SCI-Exp.).
Sabir, Z., Raja, M.A.Z., Baleanu, D., Guirao, J.L.G (2022). Neuro-swarm computational heuristic for solving a nonlinear second-order coupled Emden–Fowler model. Soft Computing, 26, 13693–13708. doi: 10.1007/s00500-022-07359-3. (indexed in SCI-Exp.).
Wischnewski, K.J., Eickhoff, S.B., Jirsa, V.K., Popovych O.V. (2022). Towards an efficient validation of dynamical whole-brain models. Scientific Reports 12, 4331. doi: 10.1038/s41598-022-07860-7. (indexed in SCI-Exp.).
Kumar, V., Kushvah B.S., Bando, M. (2022). An alternative opportunity of future Psyche mission using differential evolution and gravity assists AIMS Mathematics, 7(4), 7012-7025. doi: 10.3934/math.2022390. (indexed in SCI-Exp.).
Zhang, L., Wang, X., Dai, H., and Wei, X. (2022). A Novel Fitting Method of Electrochemical Impedance Spectroscopy for Lithium-Ion Batteries Based on Random Mutation Differential Evolution Algorithm. SAE International Journal of Electrified Vehicles, 11(2). doi: 10.4271/14-11-02-0018. (indexed in ESCI).
Abd alkreem, Z. A., Kalaf, B.A. (2021). A New Algorithm to Estimate the Parameters of Nonlinear Regression. Journal of Physics: Conference Series. Volume 1879, 032042. doi: 10.1088/1742-6596/1879/3/032042.
Strimbu, B. M., Amarioarei, A., Paun, M. (2021). Nonlinear parsimonious forest modeling assuming normal distribution of residuals. European Journal of Forest Research140, 659-674. doi: 10.1007/s10342-021-01355-2. (indexed in SCI Exp.).
Umar, M., Sabir, Z., Raja, M. A. Z., Amin, F., Saeed, T., Sanchez, Y. G. (2021). Integrated neuro-swarm heuristic with interior-point for nonlinear SITR model for dynamics of novel COVID-19. Alexandria Engineering Journal, 60(3), 2811-2824. doi: 10.1016/j.aej.2021.01.043(indexed in SCI Exp.).
Wang, M., Jia, Z., Luo, J., Chen, M., Wang, S., Ye, Z. (2021). A Hyperspectral Image Classification Method Based on Weight Wavelet Kernel Joint Sparse Representation Ensemble and β-Whale Optimization Algorithm. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,14, 2535-2550 doi: 10.1109/JSTARS.2021.3056198 (indexed in SCI Exp.).
Xie, L., Zhang, Q., Pi, D. (2021). Predicting Satellite Power System Parameter Interval Based on Optimized Kernel Extreme Learning Machine and Proportional Coefficient Method with Differential Evolution. In: Wang Y., Xu L., Yan Y., Zou J. (eds) Signal and Information Processing, Networking and Computers. Lecture Notes in Electrical Engineering, vol 677. Springer, Singapore. doi: 10.1007/978-981-33-4102-9_19
Mirsadeghi, E., Khodayifar, S. (2021). Hybridizing particle swarm optimization with simulated annealing and differential evolution. Cluster Computing, 24, 1135-1163. doi: 10.1007/s10586-020-03179-y (indexed in SCI Exp.).
Sabir, Z., Raja, M.A.Z., Guirao, J.L., Shoaib, M. (2020). Integrated intelligent computing with neuro-swarming solver for multi-singular fourth order nonlinear Emden-Fowler equation. Computational and Applied Mathematics, 39, 307. doi: 10.1007/s40314-020-01330-4 (indexed in SCI Exp.).
Al-Anber, N.J. (2020). Lomax-Rayleigh distribution: traditional and heuristic methods of estimation. Journal of Physics: Conference Series 1591(1), 012044. doi: 10.1088/1742-6596/1591/1/012044
Yonar, A., Yapici Pehlivan, N. (2020). A novel differential evolution algorithm approach for estimating the parameters of Gamma distribution: An application to the failure stresses of single carbon fibres. Hacettepe Journal of Mathematics and Statistics, 49(4), 1493-1514. DOI: 10.15672/hujms.689381 (indexed in SCI Exp.).
10. Örkcü, M., Özsoy, V.S., Örkcü, H.H. (2020).
An Optimistic-Pessimistic DEA Model Based on Game Cross Efficiency Approach.
RAIRO – Operations Research, 54(4), 1215-1230.
Thongmual, N., Laoha, C., Wichapa, N. (2024). An optimistic-pessimistic game cross-efficiency method based on a Gibbs entropy model for ranking decision making units, Bulletin of Electrical Engineering and Informatics, 13(2), 1411-1423. doi: 10.11591/eei.v13i2.5747.
Jingjing, H., Xu, Z. (2023). Dynamic Allocation Method of Incentive Pool for Financial Management Teaching Innovation Team Based on Data Mining, International Journal of Advanced Computer Science and Applications (IJACSA), 14(7), 134-145. doi: 10.14569/IJACSA.2023.0140715. (indexed in ESCI).
Li, X., Wang, H., & Yang, C. (2023). Driving mechanism of digital economy based on regulation algorithm for development of low-carbon industries. Sustainable Energy Technologies and Assessments, 55, 102909. doi: 10.1016/j.seta.2022.102909. (indexed in SCI-Exp.).
Oukil, A. (2023). Investigating prospective gains from mergers in the agricultural sector through Inverse DEA. IMA Journal of Management Mathematics, 34(3), 465–490. doi: 10.1093/imaman/dpac004. (indexed in SCI-Exp, SSCI).
Jiang, W., Liu, S. and Li, S. (2023). An extended cross-efficiency evaluation method based on information entropy with an application to the urban logistics industry. Journal of Modelling in Management, 18(2), 578-601. doi: 10.1108/JM2-11-2021-0259. (indexed in ESCI).
Guan, Q., Zou, S., Liu, H., Chen, Q. (2022). Performance Evaluation Method of Public Administration Department Based on Improved DEA Algorithm, Computational Intelligence and Neuroscience, Article ID 2338680. doi: 10.1155/2022/2338680. (indexed in SCI-Exp.).
Kremantzis M.D., Beullens, P., and Klein, J. (2022). A ranking framework based on interval self and cross-efficiencies in a two-stage DEA system. RAIRO – Operations Research, 56(3), 1293-1319. doi: 10.1051/ro/2022056. (indexed in SCI-Exp.).
Huang, Y., He, X., Dai, Y., Wang, Y.M. (2022). Hybrid game cross efficiency evaluation models based on interval data: a case of forest carbon sequestration. Expert Systems with Applications, 117521. doi: 10.1016/j.eswa.2022.117521. (indexed in SCI-Exp.).
Song, A., Huang, W., Yang, X., Tian, T., Juan, Y., Xing, Q. (2022). Two-Stage Cooperative/Non-Cooperative Game DEA Model with Decision Preference: A Case of Chinese Industrial System. Big Data Research, 28, 100303. doi: 10.1016/j.bdr.2021.100303. (indexed in SCI).
Guo, Y., & Shi, Q. (2021). Multi-objective spare parts supply network optimization: model formulation and optimal decision-making. In 2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) 135-141. doi: 10.1109/MLISE54096.2021.00031
Li, F., Wu, H., Zhu, Q., Liang, L., Kou, G. (2021). Data envelopment analysis cross efficiency evaluation with reciprocal behaviors. Annals of Operations Research. 302, 173–210. doi: 10.1007/s10479-021-04027-x (indexed in SCI Exp.).
Wu, D., Wang, Y., Liu, Y., Wu, J. (2021). DEA cross‐efficiency ranking method considering satisfaction and consensus degree. International Transactions in Operational Research, 28(6), 3470-3492. doi: 10.1111/itor.12990 (indexed in SSCI, SCI Exp.).
11. Özsoy, V.S., Örkcü, M., & Örkcü, H.H. (2020).
The most efficient supplier selection with mixed-integer two-stage data envelopment analysis model Pamukkale University Journal of Engineering Sciences. 26(4), 758-767.
Demircioğlu, Ş. N., & Özgüner, Z. (2022). Evaluation of Efficiency Measurement of Selected Technoparks with Data Envelopment Analysis (DEA). Ege Academic Review, 22(2), 155-168. doi: 10.21121/eab.925772 (indexed in ESCI).
Bulak, M.E., Sezgin, F.H., Çiftçi, F.S. (2021). Relative sustainability analysis of global-scale airports. Pamukkale University Journal of Engineering Sciences, 27(4): 504-512. doi: 10.5505/pajes.2021.06332 (indexed in ESCI).
12. Küçük, A., Özsoy, V.S., & Balkan, D. (2020).
Assessment of technical efficiency of public hospitals in Turkey European Journal of Public Health, 30(2), 230–235.
Erdebilli, B., Sicakyuz, C., & Yilmaz, İ. (2024). An integrated multiple-criteria decision-making and data envelopment analysis framework for efficiency assessment in sustainable healthcare systems. Healthcare Analytics, 100327. doi: 10.1016/j.health.2024.100327 (indexed in Scopus)
Yuan, J., Ge, Y., & Liu, Y. (2024). Research spot analysis of hospital efficiency evaluation based on CiteSpace. The Frontiers of Society, Science and Technology, 6(2), 1-7. doi: 10.25236/FSST.2024.060201
Pai, D.R., Pakdil, F. & Azadeh-Fard, N. (2024). Applications of data envelopment analysis in acute care hospitals: a systematic literature review, 1984–2022. Health Care Management Science, xx(xx), xx-xx. doi: 10.1007/s10729-024-09669-4 (indexed in SSCI)
Cinaroglu, S. (2024). Efficiency effects of public hospital closures in the context of public hospital reform: a multistep efficiency analysis. Health Care Management Science, xx(xx), xx-xx. doi: 10.1007/s10729-023-09661-4 (indexed in SSCI)
Bağcı, H. (2023). Financial Performance Evaluation of Public Oral and Dental Health Centers with Malmquist Index, Journal of Business Academy, 4(3): 328-338.doi: 10.26677/TR1010.2023.1298
Sülkü, S.N., Mortaş, A. & Küçük, A. Measuring efficiency of public hospitals under the impact of Covid-19: the case of Türkiye. Cost Effectiveness and Resource Allocation 21, 70 (2023). doi: 10.1186/s12962-023-00480-6 (indexed in SSCI)
Vahedi, S., Zahiri, M., Pirani, N. Torabipour, A. (2023). Healthcare reform and productivity of Hospital: a DEA-based analysis from South West of Iran. Cost Effectiveness and Resource Allocation, 21, 26. doi: 10.1186/s12962-022-00403-x (indexed in SSCI)
Kosycarz, E., Dędys, M., Ekes, M., Wranik, W.D. (2023). The effects of provider contract types and fiscal decentralization on the efficiency of the Polish hospital sector: A Data Envelopment Analysis across 16 health regions, Health Policy, 104714. doi: 10.1016/j.healthpol.2023.104714 (indexed in SSI, SCI-Exp.)
Bağcı, H. and Çil Koçyiğit, S. (2023). Evaluating the decentralization of public hospitals in Turkey in terms of technical efficiency: data envelopment analysis and Malmquist index. Benchmarking: An International Journal, 30 (10), 4425-4460. doi: 10.1108/BIJ-03-2021-0140 (indexed in ESCI)
Labijak-Kowalska, A., Kadziński, M., Spychała, I., Dias, L.C., Fiallos, J., Patrick, J., Michalowski, W. and Farion, K. (2023). Performance evaluation of emergency department physicians using robust value-based additive efficiency model. International Transactions in Operational Research, 30, 503-544. doi: 10.1111/itor.13099 (indexed in SSCI, SCI-Exp)
Bai, Q., Man, X., Hong, B., Li, B., Shi, X. (2022). Do China rural traditional Chinese medicine hospitals provide efficient healthcare to the people? Empirical study from 2013 to 2018 using data envelopment analysis. PLOS ONE 17(4): e0267490. doi: 10.1371/journal.pone.0267490 (indexed in SCI-Exp)
Al Subhi, M. (2022). Technical Efficiency, Productivity, and Determinants of Technical Inefficiency of Local Hospitals in Oman: Using Data Envelopment Analysis. International Journal of Social Sciences and Economic Review, 4(1), 10-16. doi: 10.36923/ijsser.v4i1.143
Bağcı, H., Çil Koçyiğit, S. (2022). Effects of reconstruction of health care on service delivery performance in Turkey: The Public Hospital Unions, Ankara Med J, 2022;(1):140-154. doi: 10.5505/amj.2022.98958
Al Subhi, M. (2022). Technical Efficiency, Productivity, and Determinants of Technical Inefficiency of Local, International Journal of Social Sciences and Economic Review, 4(1), 1-09. doi: 10.36923/ijsser.v3i4.143
Medarević A, Vuković D. (2021). Efficiency and Productivity of Public Hospitals in Serbia Using DEA-Malmquist Model and Tobit Regression Model, 2015–2019. International Journal of Environmental Research and Public Health. 18(23):12475. doi: 10.3390/ijerph182312475 (indexed in SSCI, SCI-Exp)
Durmuş, V. (2021). Differences in health literacy level of patients from public and private hospitals: a cross-sectional study in Turkey. Public Health, 200, 77-83. doi: 10.1016/j.puhe.2021.09.016 (indexed in SSCI)
Balkan, D. (2021). Comparative Study of Affiliated and Non-Affiliated Hospitals: DEA Based Analysis in Turkey American Journal of Public Health Research, 9(4), 120-129. doi: 10.12691/ajphr-9-4-1
Fazria, N., Dhamanti, I. (2021). A Literature review on the Identification of Variables for Measuring Hospital Efficiency in the Data Envelopment Analysis (DEA). Unnes Journal of Public Health, 10(1), 1-15. doi: 10.15294/ujph.v10i1.38253
Cinaroglu, S. (2021). Changes in hospital efficiency and size: An integrated propensity score matching with data envelopment analysis. Socio-Economic Planning Sciences, 76, 100960. doi: 10.1016/j.seps.2020.100960 (indexed in SSCI, SCI-Exp)
İlgün, G., Yeti̇m, B., Demi̇rci̇, Ş., Konca, M. (2020). Türkiye’deki Eğitim ve Araştirma Hastanelerinde Verimlilik Değişimi: 2014-2017 Döneminin Incelenmesi. Hacettepe Sağlık İdaresi Dergisi,23(4), 523-536.pdf: link
Taşkaya, S. (2020). Sağlıkta Dönüşüm Programının Verimlilik Düzeyinin Enflasyon Oranı ile İlişkisinin Değerlendirilmesi. Avrasya Sosyal ve Ekonomi Araştırmaları Dergisi, 7(6), 102-115.pdf: link
13. Yavuzaslanoglu, E., & Özsoy, V.S. (2020). Resistance reactions of onion landraces collected from Turkey to the stem and bulb nematode Ditylenchus dipsaci.
Mediterranean Agricultural Sciences, 33(2), 195-199.
Öcal, A. , Beşirli, G. , Evlice, E. , Yavuzaslanoğlu, E. & Elekcioğlu, İ. H. (2023). Resistance of some Turkish garlic genotypes and landraces against stem and bulb nematode, Ditylenchus dipsaci (Kühn, 1857) Filipjev, 1936 (Rhabditida: Anguinidae). Turkish Journal of Entomology, 47 (1), 111-119. doi: 10.16970/entoted.1210028 (indexed in SCI-Exp.)
14. Örkcü, H.H., Özsoy, V.S., Örkcü, M., & Bal, H. (2019). A neutral cross efficiency approach for basic two stage production systems Expert Systems with Applications, 125, 333-344.
Ganji, S.S, Dehghani, A., Ajirlu, S.F. (2024). Evaluation of intercity road passenger transportation using a novel double-frontier game-regret-cross-efficiency, Socio-Economic Planning Sciences, xx(xx), xx-xx. doi: 10.1016/j.seps.2024.101869 (indexed in SSCI, SCI-Exp)
Junior, A. P. S., Lemos, S. V., Carlucci, F. V., & Rebehy, P. C. P. W. (2024). Analysis and integration of mixed method in efficiency studies: best practices and applications in the renewable energy sector. MethodsX, 102613. doi: 10.1016/j.mex.2024.102613 (indexed in ESCI)
Huang, T., Li, S., Liu, F., Diao, H. (2024). A Slacks-Based Measure Model for Computing Game Cross-Efficiency. Systems 2024, 12, 78. doi: 10.3390/systems12030078 (indexed in SSCI)
Wang, Q., & Yang, Q. (2024). A scenario‐based requirement analysis of R&D projects from the cross‐efficiency perspective. International Transactions in Operational Research, xx(xx), xx-xx. doi: 10.1111/itor.13427 (indexed in SSCI, SCI-Exp.)
Lin, R., Li, Z. (2024). Directional distance based cross-efficiency evaluation and decomposition for parallel two-stage systems: An application to equity funds, doi: 10.1016/j.eswa.2023.122760 (indexed in SCI-Exp.)
Liu, M., Zhang, C., Huang, W., Wang, M., Xiao, G. (2024). A dynamic network data envelopment analysis cross-efficiency evaluation on the benefits of bus transit services in 33 Chinese cities, Transportation Letters, xx(xx), xx-xx. doi: 10.1080/19427867.2023.2198109 (indexed in SSCI, SCI-Exp.)
Kaçak H. (2023). Regional performance evaluation of healthcare services in Türkiye with cross-efficiency approach. Turkish Bulletin of Hygiene and Experimental Biology, 80(3): 329 - 344, doi: 10.5505/TurkHijyen.2023.34793.
Zhang, W., Wu, X., & Shi, J. (2023). Cross efficiency model of network DEA and its application on low carbon efficiency evaluation of multimodal transport. Ocean & Coastal Management, 244, 106778. doi: 10.1016/j.ocecoaman.2023.106778. (indexed in SCI-Exp.).
Li, H., Zhang, S., Deng, Y., & Wang, H. (2023). Research on the efficiency of agro-tourism integration in China: Based on the DEA cross-efficiency model. AIMS Mathematics, 8(10), 23164-23182. doi: 10.3934/math.20231178. (indexed in SCI-Exp.).
Junmei, X., Yingming, W. (2023). Measurement and Decomposition of Additive Cross Efficiency under A Two-stage Analysis Framework. Operations Research and Management Science, 32(4), 98-104. doi: 10.12005/orms.2023.0121
Jin, F., Cai, Y., Zhou, L., Ding, T. (2023). Regret-rejoice two-stage multiplicative DEA models-driven cross-efficiency evaluation with probabilistic linguistic information. Omega, 102839. doi: 10.1016/j.omega.2023.102839 (indexed in SSCI, SCI-Exp.)
Chen, Q., Chen, S., & Liu, D. (2023). Regret-based cross efficiency evaluation method in a general two-stage DEA system. Computers & Industrial Engineering 175, 108828. doi: 10.1016/j.cie.2022.108828 (indexed in SCI-Exp.)
Kiaei, H., Saen, R.F. & Matin, R.K. (2023). Cross-efficiency evaluation and improvement in two-stage network data envelopment analysis. Annals of Operations Research, 321, 281–309. doi: 10.1007/s10479-022-04861-7 (indexed in SCI-Exp.)
Ghomashi, A., & Abbasi, M. (2022). A neutral DEA model for cross-efficiency evaluation. International Journal of Research in Industrial Engineering, 11(4), 411–422. doi: 10.22105/riej.2023.343160.1314
Amirteimoori, A., Nematizadeh, M., & Nematizadeh, M. (2022). Assessment of two-stage processes cross-efficiency in the presence of undesirable factors. International Journal of Data Envelopment Analysis, 10(3), 43-56. doi:10.30495/ijdea.2022.69944.1187
Najafzade, F., Khoshfetrat, S., Saeidi, S. (2022). A cross-efficiency model using bargaining theory in a two-stage network DEA. Computational and Applied Mathematics, 41, Article number: 335. doi: 10.1007/s40314-022-02027-6. (indexed in SCI-Exp.).
Jreisat, A., Al-Mohamad, S. (2022). Is Bahrain Banking Industry Efficient?, Montenegrin Journal of Economics, 18(4), 167-179.doi: 10.14254/1800-5845/2022.18-4.14.
Tsaples, G., Papathanasiou, J., & Georgiou, A. C. (2022). An Exploratory DEA and Machine Learning Framework for the Evaluation and Analysis of Sustainability Composite Indicators in the EU. Mathematics, 10(13), 2277. doi: 10.3390/math10132277. (indexed in SCI-Exp.).
Kremantzis M.D., Beullens, P., and Klein, J. (2022). A ranking framework based on interval self and cross-efficiencies in a two-stage DEA system. RAIRO – Operations Research, 56(3), 1293-1319. doi: 10.1051/ro/2022056. (indexed in SCI-Exp.).
Zhao, Y., & Fang, L. (2022). Resource allocation for supply chains based on Pareto-optimal two-stage cross-efficiency model. RAIRO-Operations Research, 56(2), 891-910.doi: 10.1051/ro/2022029 (indexed in SCI-Exp.)
Wu, Q., Liu, X., Qin, J., Zhou, L., Garg, H., Mardani, A. (2022). Consensus reaching for prospect cross-efficiency in data envelopment analysis with minimum adjustments,Computers & Industrial Engineering (2022), doi: 10.1016/j.cie.2022.108087 (indexed in SCI-Exp.)
Ghazi, A., Lotfi, F.H. (2022). A survey on links between multiple objective decision making and data envelopment analysis. In M. Toloo, S. Talatahari & I. Rahimi (Eds.). Multi-Objective Combinatorial Optimization Problems and Solution Methods (pp. 29-70). Academic Press. (ISBN: 9780128237991). doi: 10.1016/B978-0-12-823799-1.00004-8
Kremantzis, M.D., Beullens, P., Klein, J. (2022). A fairer assessment of DMUs in a generalised two-stage DEA structure. Expert Systems with Applications. 187, 115921. doi: 10.1016/j.eswa.2021.115921 (indexed in SCI-Exp.)
Wang, M., Huang, Y., Li, D. (2021). Assessing the performance of industrial water resource utilization systems in China based on a two-stage DEA approach with game cross efficiency. Journal of Cleaner Production. 312, 127722. doi: 10.1016/j.jclepro.2021.127722 (indexed in SCI-Exp.)
An, Q., Wang, P., Yang, H., Wang, Z. (2021). Fixed cost allocation in two-stage system using DEA from a noncooperative view. OR Spectrum. 43, 1077-1102. doi: 10.1007/s00291-021-00643-y (indexed in SCI-Exp.)
Li, H., Zhao, C., Tang, X., Cheng, J., Lu, G., Gu, Y., Zhang, Z., Liu, Y. (2021). How Do Different Types of Environmental Regulations Affect Green Innovation Efficiency?. Journal of Mathematics. Volume 2021, Article ID 6677334. doi: 10.1155/2021/66773348 (indexed in SCI-Exp.)
Azadi, M. (2021). Performance Measurement of Cloud Service Suppliers and Cloud Supply Chain (Doctoral dissertation thesis, University of Technology Sydney, Sydney, New South Wales, Australia). Retrieved from http://hdl.handle.net/10453/148948
Wu, D., Wang, Y., Liu, Y., Wu, J. (2021). DEA cross‐efficiency ranking method considering satisfaction and consensus degree. International Transactions in Operational Research,28, 3470-3492. doi: 10.1111/itor.12990 (indexed in SSCI, SCI Exp.).
Davtalab-Olyaie, M., Ghandi, F., Asgharian, M. (2021). On the spectrum of achievable targets in cross-efficiency evaluation and the associated secondary goal models. Expert Systems with Applications, 177, 114927.doi: 10.1016/j.eswa.2021.114927 (indexed in SCI-Exp.)
Lin, R., Tu, C. (2021). Cross-efficiency evaluation and decomposition with directional distance function in series and parallel systems. Expert Systems with Applications, 177, 114933. doi: 10.1016/j.eswa.2021.114933 (indexed in SCI-Exp.)
Balk, B. M., De Koster, M. R., Kaps, C., Zofío, J. L. (2021). An evaluation of cross-efficiency methods: With an application to warehouse performance. Applied Mathematics and Computation, 406, 126261. doi: 10.1016/j.amc.2021.126261 (indexed in SCI-Exp.)
Zhang, X., Xia, Q., Yang, F., Song, S., Ang, S. (2021). Interval cross-efficiency for ranking decision making units using the stochastic multicriteria acceptability analysis-evidential reasoning approach. Computers & Industrial Engineering,156, 107222. doi: 10.1016/j.cie.2021.107222 (indexed in SCI-Exp.)
Li, F., Wu, H., Zhu, Q., Liang, L., Kou, G. (2021). Data envelopment analysis cross efficiency evaluation with reciprocal behaviors. Annals of Operations Research, 302, 173-210. doi: 10.1007/s10479-021-04027-x (indexed in SCI-Exp.)
Shi, H. L., Chen, S. Q., Chen, L., Wang, Y. M. (2021). A neutral cross-efficiency evaluation method based on interval reference points in consideration of bounded rational behavior. European Journal of Operational Research, 290(3), 1098-1110. doi: 10.1016/j.ejor.2020.08.055 (indexed in SCI-Exp.)
Amin, G. R., Hajjami, M. (2021). Improving DEA cross-efficiency optimization in portfolio selection. Expert Systems with Applications, 168, 114280. doi: 10.1016/j.eswa.2020.114280 (indexed in SCI-Exp.)
Chen, L., Wang, S. H., Wang, Y. M. (2020). A new data envelopment analysis clustering approach within cross-efficiency framework. Journal of the Operational Research Society, 1-10. doi: 10.1080/01605682.2020.1857667(indexed in SSCI)
Wang, L., Zhou, Z., Yang, Y., Wu, J. (2020). Green efficiency evaluation and improvement of Chinese ports: A cross-efficiency model. Transportation Research Part D: Transport and Environment, 88, 102590. doi: 10.1016/j.trd.2020.102590(indexed in SCI-Exp.)
Henriques, I. C., Sobreiro, V. A., Kimura, H., Mariano, E. B. (2020). Two-Stage DEA in Banks: Terminological Controversies and Future Directions. Expert Systems with Applications, 113632. doi: 10.1016/j.eswa.2020.113632(indexed in SCI-Exp.)
Ding, L., Yang, Y., Wang, L., Calin, A. C. (2020). Cross Efficiency Assessment of Chinauo;s marine economy under environmental governance. Ocean Coastal Management, 193, 105245. doi: 10.1016/j.ocecoaman.2020.105245(indexed in SCI-Exp.)
Behdani, Z. Darehmiraki, M. (2019) An Alternative Approach to Rank Efficient DMUs in DEA via Cross-Efficiency Evaluation, Gini Coefficient, and Bonferroni Mean. Journal of the Operations Research Society of China. doi: 10.1007/s40305-019-00264-x(indexed in ESCI)
Ding, L., Yang, Y., Wang, W., Calin, A. C. (2019). Regional carbon emission efficiency and its dynamic evolution in China: A novel cross efficiency-malmquist productivity index. Journal of Cleaner Production, 241, 118260. doi:10.1016/j.jclepro.2019.118260(indexed in SCI-Exp.)
Chen, J. X. (2019). Promoting Balance in Output Efficiencies for Cross-Efficiency Evaluation in Data Envelopment Analysis. Journal of Applied Mathematics and Physics, 7(03), 664. DOI: 10.4236/jamp.2019.73047
15. Yavuzaslanoglu, E., Sonmezoglu, O., Genc, N., Akar, Z., Ocal, A., Karaca, M., Elekcioglu, I.,Özsoy, V.S., & Aydogdu, M. (2019).
Occurrence and abundance of nematodes on onion in Turkey and their relationship with soil physicochemical properties Nematology, 21(10), 1063-1079.
Riascos-Ortiz, D. , Caicedo-Castro, J. , Arboleda-Riascos, C. , Sánchez, F. , Mosquera-Espinosa, A. and De Agudelo, F. (2023) Morpho-Molecular Characterization of Ditylenchus dipsaci and Alternatives for Its Management in Green Onion Allium fistulosum Crops from Colombia. Agricultural Sciences, 14, 1516-1534. doi: 10.4236/as.2023.1411098
Aksay, G. & Yavuzaslanoğlu, E. (2023). Status of onion production in Türkiye and in the world, effects of abiotic and biotic stress factors . Anatolian Journal of Botany, 7 (1), 32-39. doi: 10.30616/ajb.1240014
Maina, S., Karuri, H., Mugweru, J. (2022). Nematode diversity and its association with soil properties in monocrop pigeon pea. Journal of Agriculture and Food Research, 9, 100336.doi: 10.1016/j.jafr.2022.100336 (indexed in ESCI)
Öcal, A, Beşirli, G, Evlice, E, Yavuzaslanoğlu, E, Elekcioğlu, İ. (2021). Determination of the host status of some plant species with four different garlic populations of Ditylenchus dipsaci (Kühn, 1857) Filipjev, 1936 (Tylenchida: Anguinidae). Turkish Journal of Entomology, 45(3), 353-360.doi: 10.16970/entoted.918696
Yavuzaslanoglu, E., Aksay, G. (2021). Susceptibility of different plant species to two populations of Ditylenchus dipsaci Kühn, 1857 (Tylenchida: Anguinidae) from Turkey. Turkish Journal of Entomology, 45(1), 77-86.doi: 10.16970/entoted.795993
16. Alp, İ., & Özsoy, V.S. (2019).
A New Robust Bootstrap Algorithm for the Assessment of Common Set of Weights in Performance Analysis.
Iranian Journal of Management Studies, 12(2), 175-189.
Song, A. F., Yang, X., Zhang, X. Y., Wang, F., Huang, W. L. (2020). A Novel CSW Method for Data Envelopment Analysis Based on DM’s Preference Information. Journal of Computers, 31(6), 230-245. DOI: 10.3966/199115992020123106018
17. Özsoy, V.S., Örkcü, H.H., & Bal, H. (2018).
Particle Swarm Optimization Applied to Parameter Estimation of the Four-Parameter Burr III Distribution Iranian Journal of Science and Technology, Transactions A: Science, 42(2), 895-909.
Yonar, A. & Yapıcı Pehlivan, N. (2022). Evaluation and Comparison of Metaheuristic Methods to Estimate the Parameters of Gamma Distribution . Nicel Bilimler Dergisi , 4 (2) , 96-119 .doi: 10.51541/nicel.1093030.
Yonar, A., Yapici Pehlivan, N. (2020). Artificial bee colony with levy flights for parameter estimation of 3-p weibull distribution. Iranian Journal of Science and Technology, Transactions A: Science, 44, 851-864. doi: 10.1007/s40995-020-00886-4 (indexed in SCI-Exp.).
Lu, Z., Dong, L., Zhou, J. (2019). Nonlinear Least Squares Estimation for Parameters of Mixed Weibull Distributions by Using Particle Swarm Optimization. IEEE Access, 7, 60545-60554. doi:10.1109/ACCESS.2019.2915279(indexed in SCI-Exp.).
Qiu, Y., Jiang, S. (2019). Suppression of low-frequency vibration for rotor-bearing system of flywheel energy storage system. Mechanical Systems and Signal Processing, 121, 496-508. doi: 10.1016/j.ymssp.2018.11.033 (indexed in SCI-Exp.).
Rashid, H., Siam, F.M., Maan, N., (2018). Parameter Estimation for a Model of Ionizing Radiation Effects on Targeted Cells using Genetic Algorithm and Pattern Search Method. MATEMATIKA, SI, 1–13. doi: 10.11113/matematika.v34.n3.1134 (indexed in ESCI).
18. Alp, İ., & Özsoy, V.S. (2017).
Two Stage Approach for the Performances of Teams in Football Using Data Envelopment Analysis. Gazi University Journal of Science, 30(3), 195-208.
Rashid, Lord, F., Pyne, D. B., Welvaert, M., Mara, J. K. (2020). Methods of performance analysis in team invasion sports: A systematic review. Journal of Sports Sciences, 38(20), 2338-2349. DOI:10.1080/02640414.2020.1785185(indexed in SCI-Exp.).
19. Özsoy, V.S., & Örkcü, H.H. (2016).
Estimating the Parameters of Nonlinear Regression Models Through Particle Swarm Optimization Gazi University Journal of Science, 29(1), 187-199.
Tsige, M.G., Malcherek, A. & Baselt, I., (2024). An iterative approach for deriving and solving an accurate regression equation, Mathematical and Computer Modelling of Dynamical Systems, 30:1, 73-90. doi: 10.1080/13873954.2024.2313014 (indexed in SCI-Exp.)
Jalal, L., Puddu, R., Martini, M., Popescu V., and Murroni, M. (2023). A QoE Model for Mulsemedia TV in a Smart Home Environment, IEEE Transactions on Broadcasting, 69(1), 179-190. doi: 10.1109/TBC.2022.3217676 (indexed in SCI-Exp.)
Makade, R., Chakrabarti, S., Jamil, B., Katre, R. D., & Patidar, R. D. (2022, November). Development of the Empirical model and Optimization of parameters for prediction of Condenser Vacuum pressure in Thermal Power Plant. In 2022 Seventh International Conference on Parallel, Distributed and Grid Computing (PDGC) (pp. 704-707). IEEE. doi: 10.1109/PDGC56933.2022.10053126
Emmanuel, S., Okoye, I., Ezenweke, C., Shobanke, D., & Adeniyi, I. (2023). Estimating Nonlinear Regression Parameters using Particle Swarm Optimization and Genetic Algorithm: Estimating Nonlinear Regression Parameters using Particle Swarm Optimization and Genetic Algorithm. FUDMA JOURNAL OF SCIENCES, 6(6), 202 - 213. doi: 10.33003/fjs-2022-0606-1114
Kamel, M., Hanaa Abd El-Redeem Salem, H., Bary, N. A., & Bary, M. N. A. (2022). Using Johnson Schumacher Model for Parameter Estimation of Nonlinear Regression Model, 23(4), 360-373. doi: 10.21608/JSST.2022.169520.1497
Yonar, A., Yonar, H. (2022). An Efficient Hybrid Algorithm with Particle Swarm Optimization and Nelder-Mead Algorithm for Parameter Estimation of Nonlinear Regression Modeling. Gazi University Journal of Science, 35(2), 716-729. doi: 10.35378/gujs.864980 (indexed in ESCI).
Bagchi J., Si T. (2021) Nonlinear Regression Analysis Using Multi-verse Optimizer. In: Gao XZ., Kumar R., Srivastava S., Soni B.P. (eds) Applications of Artificial Intelligence in Engineering. Algorithms for Intelligent Systems. Springer, Singapore. DOI: 10.1007/978-981-33-4604-8_4
Sarı, M., Ahmad, A, Uslu, H. (2021). Medical model estimation with particle swarm optimization. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics, 70 (1), 468-482. doi: 10.31801/cfsuasmas.644071 (indexed in ESCI).
Makade, R. G., Chakrabarti, S., Jamil, B. (2020). Real-time estimation and prediction of the mortality caused due to COVID-19 using particle swarm optimization and finding the most influential parameter. Infectious Disease Modelling, 5, 772-782. DOI: 10.1016/j.idm.2020.09.003(indexed in ESCI).
Ivanov H., Anagnostopoulos C., Kolomvatsos K. (2020) In-Network Machine Learning Predictive Analytics: A Swarm Intelligence Approach. In: Mastorakis G., Mavromoustakis C., Batalla J., Pallis E. (eds) Convergence of Artificial Intelligence and the Internet of Things. Internet of Things (Technology, Communications and Computing). Springer, Cham. DOI: 10.1007/978-3-030-44907-0_7
Polatoğlu, İ., Aydın, L., Nevruz, B.C., Ozer, S. (2020). A Novel Approach for the Optimal Design of a Biosensor. Analytical Letters, 53:9, 1428-1445. doi: 10.1080/00032719.2019.1709075 (indexed in SCI-Exp.)
Al Marsoomi, I. M., Aboudi, E. H. (2020). Comparing traditional estimators and the estimators of (PSO) algorithm for some growth models of gross domestic product in Iraq. Periodicals of Engineering and Natural Sciences, 8(1), 491-507. DOI: 10.21533/pen.v8i1.1184 (indexed in Scopus).
Ahmad A.A., Sarı, M. (2019). Parameter Estimation to an Anemia Model using The Particle Swarm Optimization. Sigma Journal of Engineering and Natural Sciences, 37(4), 1331-1343. (indexed in ESCI).
Mahaboob, B., Venkateswarlu, B., Ravi Sankar, J., Peter Praveen, J., Narayana C. (2018). A Discourse on the Estimation of Nonlinear Regression Model. International Journal of Engineering & Technology, 7(4), 992-994. DOI: 10.14419/ijet.v7i4.10.26642.
Mortazavi, A., Toğan, V., Daloğlu, A., Nunhoğlu, A. (2018). Comparison of Two Metaheuristic Algorithms on Sizing and Topology Optimization of Trusses and Mathematical Functions. Gazi University Journal of Science, 31(2), 416-435. (indexed in ESCI).
Jasim Hassan Lazem Sabah Manfi Reza (2018). Comparison of Some Artificial Intelligence Algorithms with the Two Non-Linear Least Squares Method and the Maximum Likelihood Method of Estimating the Ratkowsky Model Using Simulation. Journal of Al-Rafidain University Faculty of Science, 43, 126-150.
Jalal, L. (2018) Quality of Experience Methods and Models for Multi-Sensorial Media, Ph.D. Thesis in Dept. of Electrical and Electronic Engineering University of Cagliari.
Dorneles, N. A. (2018). Avaliação de algoritmos para determinação de regiões de confiança de parâmetros. Ph.D. Thesis UNIVERSIDADE FEDERAL DO RIO GRANDE DO SUL ESCOLA DE ENGENHARIA DEPARTAMENTO DE ENGENHARIA QUÍMICA.
Michailidis, P. D. (2018). A Preliminary Performance Study on Nonlinear Regression Models using the Jaya Optimisation Algorithm. International Journal of Applied Mathematics, 48(4). (indexed in Scopus).
Demirel, G., Acar, E., Celebioglu, K., Aradag, S. (2017). CFD-driven surrogate-based multi-objective shape optimization of an elbow type draft tube. International Journal of Hydrogen Energy, 42(28), 17601-17610. DOI: 10.1016/j.ijhydene.2017.03.082(indexed in SCI).
Jalal, L., Popescu, V., Murroni, M. (2017). Quality-of-experience parameter estimation for multisensorial media using Particle Swarm Optimization. In Optimization of Electrical and Electronic Equipment (OPTIM) 2017 Intl Aegean Conference on Electrical Machines and Power Electronics (ACEMP), 2017 International Conference on, 965-970. IEEE. DOI:10.1109/OPTIM.2017.7975095(indexed in SCI).
Jalal, L., Murroni, M. (2017). A nonlinear quality of experience model for high dynamic spatio-temporal mulsemedia. InQuality of Multimedia Experience (QoMEX), 2017 Ninth International Conference on, 1-6. IEEE. DOI: 10.1109/QoMEX.2017.7965654(indexed in SCI).
Balku, Ş. (2017). Analysis of combined cycle efficiency by simulation and optimization. Energy Conversion and Management, 148, 174-183. doi: 10.1016/j.enconman.2017.05.032(indexed in SCI)
20. Bal, H. & Özsoy, V.S.(2016). Selecting DEA model specifications and ranking units via principal component analysis: an application of economic performance of cities.
Dumlupinar University Journal of Social Science, SI, 125-135.
Pınarbaşı, A. , Aydın, U. , Karadayı, M. A. & Tozan, H. (2022). An Integrated Performance Measurement Framework for Restaurant Chains: A Case Study In Istanbul. Journal of Industrial Engineering, 33(3), 484-499. doi: 10.46465/endustrimuhendisligi.1087736
21. Gölcükcü, A., Özsoy, V.S., Bal, H. (2016, September 6-8).
Matrix model for multiple group comparison with malmquist data envelopment analysis: A basketball league example.
OR58: 58th Annual Conference on Operational Research Society, pp. 21 - 29, Portsmouth.
Karaibrahimoglu, A., Öztürk, S.A., Gölcükcü, A. (2021). An Innovative Study to Evaluate the Treatment Efficiency of Percutaneous Nephrolithotomy by DEA. Journal of Biostatistics and Biometric Applications, 6(1), 105. doi: 10.15744/2455-765X.6.105
22. Örkcü, H.H., Özsoy, V.S., Aksoy, E., & Doğan, M. I. (2015).
Estimating the parameters of 3-p Weibull distribution using particle swarm optimization: A comprehensive experimental comparison Applied Mathematics and Computation, 268, 201-226.
2024
Jabbar, N. A. A., & Kalaf, B. A. (2024, March). Some meta heuristic algorithms to estimate the survival function of truncated inverse Gompertz distribution. In AIP Conference Proceedings 3036(1): 040024. doi: 10.1063/5.0202793
Zhong, J., He, D., Jin, Z., Sun, H., & Shan, S. (2024). Reliability model for key components of urban rail transit train based on improved hunter-prey optimization. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 1748006X241231067.doi: 10.1177/1748006X241231067 (indexed in SCI-Exp.)
Guo, J., Kong, X., Wu, N. et al. (2023). Weibull parameter estimation and reliability analysis with small samples based on successive approximation method. Journal of Mechanical Science and Technology, 37 (11). doi: 10.1007/s12206-023-1019-z (indexed in SCI-Exp.)
Yang, X., Xie, L., Liu, Y., & Zhao, B. (2023). A Review of Parameter Estimation Methods of the Three-Parameter Weibull Distribution. In 2023 9th International Symposium on System Security, Safety, and Reliability (ISSSR), 20-31. IEEE. doi: 10.1109/ISSSR58837.2023.00013
Yang, X., Xie, L., Song, J., Zhao, B., Li, Y. (2023) On interval estimation methods for the location parameter of the Weibull distribution: An application to alloy material fatigue failure data, Communications in Statistics - Theory and Methods, doi: 10.1080/03610926.2023.2242984 (indexed in SCI-Exp.)
Han, F., Li, X., Qi, S., Wang, W., Shi, W. (2023). Reliability analysis of wind turbine subassemblies based on the 3-P Weibull model via an ergodic artificial bee colony algorithm, Probabilistic Engineering Mechanics, 103476. doi: 10.1016/j.probengmech.2023.103476 (indexed in SCI-Exp.)
Vanem, E. (2023). Analyzing Extreme Sea State Conditions by Time-Series Simulation Accounting for Seasonality. Journal of Offshore Mechanics and Arctic Engineering, 145(5), 051201. doi: 10.1115/1.4056786 (indexed in SCI-Exp.)
Kılıç, M.B. (2023). Artificial Bee Colony and Genetic Algorithms for Parameters Estimation of Weibull Distribution. In: Biswas, A., Kalayci, C.B., Mirjalili, S. (eds) Advances in Swarm Intelligence. Studies in Computational Intelligence, vol 1054. Springer, Cham. doi: 10.1007/978-3-031-09835-2_17
Koçak, E. , Demir Yurtseven, E. & Örkcü, H. H. (2022). A Multi-Objective Programming Approach to Weibull Parameter Estimation. Hacettepe Journal of Mathematics and Statistics, 51(2), 543-558. doi: 10.15672/hujms.912435 (indexed in SCI-Exp.)
Raikar, S. L., & Gaonkar, R. S. P. (2022). Jaya algorithm in estimation of P [X> Y] for two parameter Weibull distribution. AIMS Mathematics, 7(2), 2820-2839.doi: 10.3934/math.2022156 (indexed in SCI-Exp.)
Yonar, A. & Yapıcı Pehlivan, N. (2022). Evaluation and Comparison of Metaheuristic Methods to Estimate the Parameters of Gamma Distribution. Nicel Bilimler Dergisi, 4(2), 96-119. doi: 10.51541/nicel.1093030.
Seo, K., & Lee, W. (2022). Optimal Experimental Designs for Clustered Read-Out Data of Reliability Tests Via Particle Swarm Optimization. Computers & Industrial Engineering, 171, 108471. doi: 10.1016/j.cie.2022.108471 (indexed in SCI-Exp.)
Koçak, E., Örkcü, H.H. (2022). A multi-objective programming-based approach to material life prediction: Weibull distribution application. Journal of The Faculty of Engineering and Architecture of Gazi University, 37(4), 1783-1792. doi: 10.17341/gazimmfd.918607 (indexed in SCI-Exp.)
Yonar, A., Yonar, H. (2022). An Efficient Hybrid Algorithm with Particle Swarm Optimization and Nelder-Mead Algorithm for Parameter Estimation of Nonlinear Regression Modeling. Gazi University Journal of Science, 35(2), 716-729. doi: 10.35378/gujs.864980 (indexed in eSCI)
Fontes, E.A. de S., Silva, M.C.M. da., Rodrigues, L.T., Mendonça, G.O.R., Mello, C.C.C. de S. (2022). Parameter analysis in the optimization of concrete beams using the algorithm Colony of Fireflies. Research, Society and Development, 11(2), e36611225851. doi: 10.33448/rsd-v11i2.25851
Ouédraogo, J.E.O., Katchekpele E., Bere, F. (2021). Efficient bounds for scale and threshold parameters in the case of the three-parameter Weibull distribution, Far East Journal of Theoretical Statistics, 63(1), 19-28. doi: 10.17654/TS063010019
Yonar, A. & Yapıcı Pehlivan, N. (2021). Parameter Estimation Based on Maximum Likelihood Estimation Method for Weibull Distribution using Dragonfly Algorithm. Mugla Journal of Science and Technology, 7(2), 84-90. doi: 10.22531/muglajsci.973403
Ouedraogo, O. J.E. , Katchekpele, E. & Kpanzou, T. A. (2021). Adaptative bounds and estimation in the case of the three-parameter Weibull distribution. Afrika Statistika 16(4), 2979 - 2991. doi: doi: 10.16929/as/2021.2979.191
Reddy, G. H., Koundinya, A. N., Raju, M., Gope S., Behera, C. (2021). Lifetime Estimation of Electrical Equipment in Distribution system using Modified 3-Parameter Weibull Distribution. 2021 International Conference on Design Innovations for 3Cs Compute Communicate Control (ICDI3C), 21-26. doi: 10.1109/ICDI3C53598.2021.00013
Anis Ben Abdessalem (2021). Estimating the parameters of parametric lifetime distributions through an efficient acceptance–rejection sampler. Engineering Applications of Artificial Intelligence, Volume 106, November 2021, 104457. doi: 10.1016/j.engappai.2021.104457 (indexed in SCI-Exp).
Zhou, D., Zhuang, X., Zuo, H. (2021). A Novel Three-parameter Weibull Distribution Parameter Estimation Using Chaos Simulated Annealing Particle Swarm Optimization in Civil Aircraft Risk Assessment. Arabian Journal for Science and Engineering, 1-18. doi: 10.1007/s13369-021-05467-0 (indexed in SCI-Exp.).
Çankaya, M. N. (2021). On the estimating equations and objective functions for parameters of exponential power distribution: Application for disorder.
Yang, X. (2020). Application of PSO Method for Archimedean Copula Parameter Estimation in Flood (Rain) and Tide Joint Distribution Analysis. Journal of Hydrologic Engineering, 26(3), 05020052. doi: 10.1061/(ASCE)HE.1943-5584.0002056 (indexed in SCI-Exp.).
Krygin, A. (2020). Evaluation of The Weibull Distribution Parameters for Small Volume OF the sample in the Problems of Optimization of Planning Repair Works on Engineering Networks. Management of Large Systems: Proceedings, 84, 177-197. doi: 10.25728/ubs.2020.84.9
Torres, M. K. V., Cruz, J. F. O., Silupu, W. C. (2020). Optimization of the replacement time for critical repairable components. DYNA, 87(214), 93-99. doi: 10.15446/dyna.v87n214.84509
Çankaya, M. N. (2020). M-Estimations of Shape and Scale Parameters by Order Statistics in Least Informative Distributions on q-deformed logarithm. Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 10(3), 1984-1996. doi: 10.21597/jist.720712
Çankaya, M. N. (2020). On the Robust Estimations of Location and Scale Parameters for Least Informative Distributions. Turkish Journal of Science and Technology, 15(2), 71-78. pdf: link
Júnior, S. F. A. X., Xavier, É. F. M., da Silva Jale, J., de Oliveira, T. A., Sabino, A. L. C. (2020). An application of Particle Swarm Optimization (PSO) algorithm with daily precipitation data in Campina Grande, Paraíba, Brazil. Research, Society and Development, 9(8), e444985841-e444985841. doi: 10.33448/rsd-v9i8.5841
Liu Y, Wang Y, Fan Z, Chen X, Zhang C, Tan Y. (2020). A new universal multi-stress acceleration model and multi-parameter estimation method based on particle swarm optimization. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 234(6):764-778. doi:10.1177/1748006X20918793 (indexed in SCI-Exp.).
Yonar, A., Yapici Pehlivan, N. (2020). Artificial bee colony with levy flights for parameter estimation of 3-p weibull distribution. Iranian Journal of Science and Technology, Transactions A: Sciencei, 44, 851-864. doi: 10.1007/s40995-020-00886-4 (indexed in SCI-Exp.).
Vanem, E., Gramstad, O., Bitner-Gregersen, E. M. (2019). A simulation study on the uncertainty of environmental contours due to sampling variability for different estimation methods. Applied Ocean Research, 91, 101870. doi: 10.1016/j.apor.2019.101870(indexed in SCI-Exp.).
Liu, Q.B, Shi, W.K., Chen, Z.Y., Luo, L.M., Su, Z.Y., Huang, K.J. (2019) Parameter estimation of mixed reliability model based on kernel density optimal grouping and gravity search algorithm. Journal of Jilin University (Engineering and Technology Edition), 49 (6), 1818-1825. doi:10.13229/j.cnki.jdxbgxb20180978
Zhang, J., Zhao, Y., Liu, M., Kong, L. (2019). A Tukey’s g-and-h distribution based approach with PSO for degradation reliability modeling. Engineering Computations. doi: 10.1108/EC-11-2017-0428(indexed in SCI-Exp.).
Prasojo, A.P.S. Prasetyoputra, P. (2019). PSO-KS Algorithm for Fitting Lognormal Distribution: Simulation and Empirical Implementation to Women’s Age at First Marriage Data. IOP Conference Series: Materials Science and Engineering, vol. 546, 052052. doi: 10.1088/1757-899x/546/5/052052
Lu, Z., Dong, L., Zhou, J. (2019). Nonlinear Least Squares Estimation for Parameters of Mixed Weibull Distributions by Using Particle Swarm Optimization. IEEE Access, 7, 60545-60554. doi:: 10.1109/ACCESS.2019.2915279(indexed in SCI-Expanded).
Yang, F., Ren, H., Hu, Z. (2019). Maximum Likelihood Estimation for Three-Parameter Weibull Distribution Using Evolutionary Strategy. Mathematical Problems in Engineering, vol. 2019, Article ID 6281781, 8 pages, 2019. doi: 10.1155/2019/6281781(indexed in SCI-Expanded).
Acitas, S., Aladag, C. H., Senoglu, B. (2019). A new approach for estimating the parameters of Weibull distribution via particle swarm optimization: An application to the strengths of glass fibre data. Reliability Engineering System Safety, 183, 116-127. (indexed in SCI) doi: 10.1016/j.ress.2018.07.024
Liu, Q., Shi, W., Chen, Z., Shang, G. (2018). Statistical distribution modeling and two-step parameter identification of vehicle bridge displacement spectrum. Transactions of the Chinese Society of Agricultural Engineering, 34 (23), pp. 67-75. doi: 10.11975/j.issn.1002-6819.2018.23.008
Wu, Z., Zhang, S., Wang, T. (2018). A cooperative particle swarm optimization with constriction factor based on simulated annealing. Computing, 100:861-880. (indexed in SCI) doi: 10.1007/s00607-018-0625-6
Özsoy, V. S., Örkcü, H. H., Bal, H. (2018). Particle Swarm Optimization Applied to Parameter Estimation of the Four-Parameter Burr III Distribution. Iranian Journal of Science and Technology, Transactions A: Science, 42(2), 895-909. (indexed in SCI-Exp.) doi: 10.1007/s40995-017-0230-0
Yang, Y. (2017). A Clustering Method Based on PSO-GA Optimization Algorithm. Revista de la Facultad de Ingeniería, 32(10), 85-91.
Handoyo, S., Efendi, A., Jie, F., Widodo, A. (2017). Implementation Of Particle Swarm Optimization (PSO) Algorithm For Estimating Parameter Of Arma Model Via Maximum Likelihood Method. Far East Journal of Mathematical Sciences, 102(7), 1337-1363. doi: 10.17654/MS102071337(indexed in Scopus)
Okagbue, H. I., Adamu, M. O., Opanuga, A. A., Oghonyon, J. G. (2017). Classes of Ordinary Differential Equations Obtained for the Probability Functions of 3-Parameter Weibull Distribution. In Proceedings of the World Congress on Engineering and Computer Science (Vol. 2). (indexed in Web of Science)
Mokhtari, H., Salmasnia, A. (2017). Fitting the Three-parameter Weibull Distribution by using Greedy Randomized Adaptive Search Procedure. International Journal of Engineering-Transactions C: Aspects, 30(3), 424-431. (indexed in ESCI)
Okagbue, H. I., Adamu, M. O., Opanuga, A. A., Oghonyon, J. G., Adamu, P. I. (2017, October). 3-Parameter Weibull Distribution: Ordinary Differential Equations. In The World Congress on Engineering and Computer Science (pp. 377-388). Springer, Singapore. doi: 10.1007/978-981-13-2191-7_27
Alavi, O., and Hooshmand Viki, A. (2017). A particle swarm optimization-based flowchart to select wind speed distribution function. International Journal of Energy and Statistics, 5(1), 1750003 doi: 10.1142/S233568041750003X(indexed in ESCI).
Li, Y., Yu, W., Li, B., Yao, R. (2016). A multidimensional model for green building assessment: A case study of a highest-rated project in Chongqing. Energy and Buildings, 125, 231-243. doi: 10.1016/j.enbuild.2016.04.055(indexed in SCI)
Meng, W., Tonghai, J., Xiao, L., Yan, Z., Haiwei, W., (2016). Image Segmentation Based on Adaptive Inertia Weight Particle Swarm Optimization. Rev. Téc. Ing. Univ. Zulia, 39(2), 235-241. (indexed in SCOPUS).