uluslararası yayınlar

Erken Erişim (Early Access)

  • Ayyildiz, E., & Erdogan, M. Identifying and prioritizing the factors to determine best insulation material using Bayesian best worst method. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineeringhttps://doi.org/10.1177/09544089221111586
  • Gürsoy Yılmaz, B., Yılmaz, Ö., F., & Yeni, F., B. (2024). Comparison of lot streaming division methodologies for multi-objective hybrid flowshop scheduling problem by considering limited waiting time. Journal of Industrial and Management Optimization. https://www.aimsciences.org/article/doi/10.3934/jimo.2024058
  • Bouraima, M. B., Ayyildiz, E., Ozcelik, G., Tengecha, N. A., & Stevic, Z. (2024). Alternative prioritization for mitigating urban transportation challenges using a Fermatean fuzzy-based intelligent decision support model. Neural Computing and Applications, 1-15. https://doi.org/10.1007/s00521-024-09463-x
  • Bouraima, M. B., Oyaro, J., Ayyildiz, E., Erdogan, M., & Ndiema, K. M. (2023). An Integrated Decision Support Model for effective Institutional Coordination Framework in Public Transportation Planning. https://doi.org/10.1007/s00500-023-09425-
  • Imamoglu, G., Ayyildiz, E., Aydin, N., & Topcu, Y. I. (2024). Bloodmobile location selection for resilient blood supply chain: a novel spherical fuzzy AHP-integrated spherical fuzzy COPRAS methodology. Journal of Enterprise Information Managementhttps://doi.org/10.1108/JEIM-07-2023-0379
  • Ozcelik, G., & Sahin, A. (2024). Ensuring sustainable strategies for achieving multi-commodity maximum flow on a fuzzy network under interdictions. Scientia Iranicahttps://doi.org/10.24200/SCI.2024.60981.708


  • Bouraima, M. B., Ayyıldız, E., Badi, I., Özçelik, G., Yeni, F. B., & Pamucar, D. (2024). An integrated intelligent decision support framework for the development of photovoltaic solar power. Engineering Applications of Artificial Intelligence, 127(A), 107253. https://doi.org/10.1016/j.engappai.2023.107253
  • Sahin, A., Imamoglu, G., Murat, M., & Ayyildiz, E. (2024). A holistic decision-making approach to assessing service quality in higher education institutions. Socio-Economic Planning Sciences, 101812. https://doi.org/10.1016/J.SEPS.2024.101812


  • Ayyildiz, E. (2023). Interval valued intuitionistic fuzzy analytic hierarchy process-based green supply chain resilience evaluation methodology in post COVID-19 era. Environmental Science and Pollution Research, 30, 42476?42494. https://doi.org/10.1007/s11356-021-16972-y
  • Ayyildiz, E., & Erdogan, M. (2023). A decision support mechanism in the determination of organic waste collection and recycling center location: A sample application for Turkiye. Applied Soft Computing, 110752. https://doi.org/10.1016/j.asoc.2023.110752
  • Ayyildiz, E., Murat, M., Imamoglu, G., & Kose, Y. (2023). A novel hybrid MCDM approach to evaluate universities based on student perspective. Scientometrics, 128(1), 55-86. https://doi.org/10.1007/s11192-022-04534-z
  • Ayyildiz, E., Yildiz, A., Taskin, A., & Ozkan, C. (2023). An interval valued Pythagorean Fuzzy AHP integrated Quality Function Deployment methodology for Hazelnut Production in Turkey. Expert Systems with Applications, 120708. https://doi.org/10.1016/j.eswa.2023.120708
  • Bouraima, M. B., Gore, A., Ayyildiz, E., Yalcin, S., Badi, I., Kiptum, C. K., & Qiu, Y. (2023). Assessing of causes of accidents based on a novel integrated interval-valued Fermatean fuzzy methodology: towards a sustainable construction site. Neural computing and applications35(29), 21725-21750. https://doi.org/10.1007/s00521-023-08948-5
  • Bouraima, M. B., Qiu, Y., Ayyildiz, E., & Yildiz, A. (2023). Prioritization of strategies for a sustainable regional transportation infrastructure by hybrid spherical fuzzy group decision-making approach. Neural Computing and Applications, 1-20. https://doi.org/10.1007/s00521-023-08660-4
  • Cicekdagi, H.I., Ayyildiz, E. & Akkoyunlu, M.C. (2023). Enhancing search and rescue team performance: investigating factors behind social loafing. Natural Hazards, 1-26. https://doi.org/10.1007/s11069-023-06164-x
  • Demir, A., Demirkir, C., Ozsahin, S., & Aydin, I. (2023). Artificial neural-network optimisation of nail size and spacings of plywood shear wall. Wood Material Science & Engineering18(1), 97-106. https://doi.org/10.1080/17480272.2021.1992648
  • Es, H. A., Baban, P., & Hamzacebi C. (2023). Prediction of natural gas demand by considering implications of energy-related policies: The case of Türkiye, Energy Sources, Part B: Economics, Planning, and Policy, 18:1, https://doi.org/10.1080/15567249.2023.22748
  • Gürsoy Yılmaz, B., Yılmaz, Ö. F., & Çevikcan, E. (2023). Lot streaming in workforce scheduling problem for seru production system under Shojinka philosophy. Computers & Industrial Engineering, 185, 109680. https://doi.org/10.1016/j.cie.2023.109680
  • Imamoglu, G., Topcu, Y. I., & Aydin, N. (2023). A Systematic Literature Review of the Blood Supply Chain through Bibliometric Analysis and Taxonomy. Systems, 11(3), 124. https://doi.org/10.3390/systems11030124
  • Kayaturan, G. Ç., Özçelik, G., & Gökçe, A. (2023). Decision-aided evaluation of paths for routing on multi-attribute computer network consisting of encoded paths under uncertainty. Expert Systems with Applications, 119881. https://doi.org/10.1016/j.eswa.2023.119881
  • Kose, Y., Cevikcan, E., Ertemel, S., & Murat, M. (2023). Game theory-oriented approach for disassembly line worker assignment and balancing problem with multi-manned workstations. Computers & Industrial Engineering181, 109294. https://doi.org/10.1016/j.cie.2023.109294
  • Oksuz, M. K., Buyukozkan, K., Bal, A., & Satoglu, S. I. (2023). A genetic algorithm integrated with the initial solution procedure and parameter tuning for capacitated P-median problem. Neural Computing and Applications, 35, 6313?6330. http://doi.org/10.1007/s00521-022-08010-w
  • Sahmutoglu, I., Taskin, A., & Ayyildiz, E. (2023). Assembly area risk assessment methodology for post-flood evacuation by integrated neutrosophic AHP-CODAS. Natural Hazards,  116, 1071?1103. https://doi.org/10.1007/s11069-022-05712-1
  • Singer, H. & Ozsahin, S. (2023) Applying an interval-valued Pythagorean fuzzy analytic hierarchy process to rank factors influencing wooden outdoor furniture selection, Wood Material Science & Engineering, 18:1, 322-333. 10.1080/17480272.2021.2025427
  • Yalcin Kavus, B., Ayyildiz, E., Gulum Tas, P., & Taskin, A. (2023). A hybrid Bayesian BWM and Pythagorean fuzzy WASPAS-based decision-making framework for parcel locker location selection problem. Environmental Science and Pollution Research, 30(39), 90006-90023. https://doi.org/10.1007/s11356-022-23965-y
  • Yılmaz, Ö. F., Yeni, F. B., Yılmaz, B. G., & Özçelik, G. (2023). An optimization-based methodology equipped with lean tools to strengthen medical supply chain resilience during a pandemic: A case study from Turkey. Transportation Research Part E: Logistics and Transportation Review, 173, 103089. https://doi.org/10.1016/j.tre.2023.103089


  • Ayyildiz, E., & Taskin, A. (2022). Humanitarian Relief Supply Chain Performance Evaluation by a Scor Based Trapezoidal Type-2 Fuzzy Multi-Criteria Decision Making Methodology: An Application in Turkey. Scientia Iranicahttps://doi.org/10.24200/sci.2020.52592.2786  
  • Ayyildiz, E. (2022). Fermatean fuzzy step-wise Weight Assessment Ratio Analysis (SWARA) and its application to prioritizing indicators to achieve sustainable development goal-7. Renewable Energy, 193, 136-148. https://doi.org/10.1016/j.renene.2022.05.021
  • Ayyildiz, E., & Taskin, A. (2022). A novel spherical fuzzy AHP-VIKOR methodology to determine serving petrol station selection during COVID-19 lockdown: A pilot study for İstanbul. Socio-Economic Planning Sciences, 83, 101345. https://doi.org/10.1016/j.seps.2022.101345
  • Ayyildiz, E. (2022). A novel pythagorean fuzzy multi-criteria decision-making methodology for e-scooter charging station location-selection. Transportation Research Part D: Transport and Environment, 111, 103459. https://doi.org/10.1016/j.trd.2022.103459
  • Ayyıldız, E., Taşkın, A., Yıldız, A., & Özkan, C. (2022). Artificial neural networks integrated mixed integer mathematical model for multi-fleet heterogeneous time-dependent cash in transit problem with time windows. Neural Computing and Applications, 34(24), 21891-21909. https://doi.org/10.1007/s00521-022-07659-7
  • Baysal, M. E., Sarucan, A., Büyüközkan, K., & Engin, O., (2022). Artificial bee colony algorithm for solving multi-objective distributed fuzzy permutation flow shop problem. Journal Of Intelligent & Fuzzy Systems, vol.42, no.1, 439-449. https://doi.org/10.3233/JIFS-219202
  • Erdogan, M., & Ayyildiz, E. (2022). Investigation of the pharmaceutical warehouse locations under COVID-19?A case study for Duzce, Turkey. Engineering Applications of Artificial Intelligence, 116, 105389. https://doi.org/10.1016/j.engappai.2022.105389
  • Erdogan, M., & Ayyildiz, E. (2022). Comparison of hospital service performances under COVID-19 pandemics for pilot regions with low vaccination rates. Expert Systems with Applications, 206, 117773. https://doi.org/10.1016/j.eswa.2022.117773
  • Gürsoy Yılmaz, B., & Yılmaz, Ö. F., (2022). Lot streaming in hybrid flowshop scheduling problem by considering equal and consistent sublots under machine capability and limited waiting time constraint. Computers & Industrial Engineering, vol.173. https://doi.org/10.1016/j.cie.2022.108745
  • Kavus, B. Y., Tas, P. G., Ayyildiz, E., & Taskin, A. (2022). A three-level framework to evaluate airline service quality based on interval valued neutrosophic AHP considering the new dimensions. Journal of Air Transport Management, 99, 102179. https://doi.org/10.1016/j.jairtraman.2021.102179
  • Kose, Y., Civan, H. N., Ayyildiz, E., & Cevikcan, E. (2022). An Interval Valued Pythagorean Fuzzy AHP?TOPSIS Integrated Model for Ergonomic Assessment of Setup Process under SMED. Sustainability, 14(21), 13804. https://doi.org/10.3390/su142113804
  • Özçelik, G. (2022). The attitude of MCDM approaches versus the optimization model in finding the safest shortest path on a fuzzy network. Expert Systems with Applications203, 117472. https://doi.org/10.1016/j.eswa.2022.117472
  • Özşahin, Ş., & Singer, H., (2022). Prediction of noise emission in the machining of wood materials by means of an artificial neural network. New Zealand Journal of Forestry Science, vol.52, 1-10. http://doi.org/10.33494/nzjfs522022x92x
  • Singer, H., & Özşahin, Ş., (2022). Prioritization of laminate flooring selection criteria from experts' perspectives: a spherical fuzzy AHP-based model. Architectural Engineering and Design Management, vol.18, no.6, 911-926. http://doi.org/10.1080/17452007.2021.1956421
  • Singer, H., & Özşahin, Ş., (2022). Prioritization of factors affecting surface roughness of wood and wood-based materials in CNC machining: a fuzzy analytic hierarchy process model. Wood Material Science & Engineering, vol.17, no.2, 63-71. http://doi.org/10.1080/17480272.2020.1778079
  • Yıldız, A., Ayyıldız, E., Taşkın, A., & Özkan, C. (2022). Evaluation of quality expectations for intercity bus firms by interval type-2 trapezoidal fuzzy AHP and firm selection. Journal of the Faculty of Engineering and Architecture of Gazi University, 37(2), 757-770. https://doi.org/10.17341/gazimmfd.625921
  • Yıldız, A., Guneri, A. F., Ozkan, C., Ayyildiz, E., & Taskin, A. (2022). An integrated interval-valued intuitionistic fuzzy AHP-TOPSIS methodology to determine the safest route for cash in transit operations: a real case in Istanbul. Neural Computing and Applications, 34(18), 15673-15688. https://doi.org/10.1007/s00521-022-07236-y
  • Yılmaz, Ö. F., & Yazıcı, B., (2022). Tactical level strategies for multi-objective disassembly line balancing problem with multi-manned stations: an optimization model and solution approaches. Annals of Operations Research, vol.319, no.2, 1793-1843. https://doi.org/10.1007/s10479-020-03902-3
  • Yılmaz, Ö. F., (2022). An integrated bi-objective U-shaped assembly line balancing and parts feeding problem: optimization model and exact solution method. Annals of Mathematics and Artificial Intelligence, vol.90, no.7-9, 679-696. https://doi.org/10.1007/s10472-020-09718-y


  • Ayyildiz, E., & Taskin Gumus, A. (2021). Pythagorean fuzzy AHP based risk assessment methodology for hazardous material transportation: an application in Istanbul. Environmental Science and Pollution Research, 28, 35798-35810. https://doi.org/10.1007/s11356-021-13223-y
  • Ayyildiz, E., & Taskin Gumus, A. (2021). A novel distance learning ergonomics checklist and risk evaluation methodology: A case of Covid?19 pandemic. Human Factors and Ergonomics in Manufacturing & Service Industries, 31(4), 397-411. https://doi.org/10.1002/hfm.20908
  • Ayyildiz, E., & Taskin Gumus, A. (2021). Interval-valued Pythagorean fuzzy AHP method-based supply chain performance evaluation by a new extension of SCOR model: SCOR 4.0. Complex & Intelligent Systems, 7, 559-576. https://doi.org/10.1007/s40747-020-00221-9
  • Ayyildiz, E., Yildiz, A., Taskin Gumus, A., & Ozkan, C. (2021). An integrated methodology using extended SWARA and DEA for the performance analysis of wastewater treatment plants: Turkey case. Environmental Management, 67(3), 449-467. https://doi.org/10.1007/s00267-020-01381-7
  • Ayyildiz, E., Erdogan, M., & Taskin, A. (2021). Forecasting COVID-19 recovered cases with Artificial Neural Networks to enable designing an effective blood supply chain. Computers in Biology and Medicine, 139, 105029. https://doi.org/10.1016/j.compbiomed.2021.105029
  • Ayyildiz, E., Erdogan, M., & Taskin Gumus, A. (2021). A Pythagorean fuzzy number-based integration of AHP and WASPAS methods for refugee camp location selection problem: a real case study for Istanbul, Turkey. Neural Computing and Applications, 33(22), 15751-15768. https://doi.org/10.1007/s00521-021-06195-0
  • Es, H. A., (2021). A hybrid approach based on machine learning in determining the effectiveness of hydroelectric power plants.  International Journal of Industrial Engineering-Theory Applications and Practice, vol.28, no.5, 477-489. https://doi.org/10.23055/ijietap.2021.28.5.7783
  • Es, H. A., (2021). Monthly natural gas demand forecasting by adjusted seasonal grey forecasting model.  Energy Sources Part A - Recovery Utilization and Environmental Effects, vol.43, no.1, 54-69. https://doi.org/10.1080/15567036.2020.1831656
  • Es, H. A., & Hamzacebi, C. (2021). Exploring CO2 emissions according to planned energy investments and policies: the case of Turkey. Soft Computing, 25(1), 785-798.  https://doi.org/10.1007/s00500-020-05208-9
  • Gulum, P., Ayyildiz, E., & Gumus, A. T. (2021). A two-level interval valued neutrosophic AHP integrated TOPSIS methodology for post-earthquake fire risk assessment: An application for Istanbul. International Journal of Disaster Risk Reduction, 61, 102330. https://doi.org/10.1016/j.ijdrr.2021.102330
  • Özçelik, G., & Nalkıran, M. (2021). An extension of EDAS method equipped with trapezoidal bipolar fuzzy information: an application from healthcare system. International Journal of Fuzzy Systems23(7), 2348-2366. https://doi.org/10.1007/s40815-021-01110-0
  • Özçelik, G., Yılmaz, Ö. F., & Yeni, F. B. (2021). Robust optimisation for ripple effect on reverse supply chain: an industrial case study. International Journal of Production Research59(1), 245-264. https://doi.org/10.1080/00207543.2020.1740348
  • Özşahin, Ş., & Singer, H. (2021). The use of an artificial neural network for predicting the gloss of thermally densified wood veneers. Baltic Forestry27(2), 271-278.
  • Tükenmez, İ., & Kaya, O. (2021). A sustainable vehicle routing problem with alternative road and speed options. Journal of the Faculty of Engineering and Architecture of Gazi University36(4), 2037-2051. https://doi.org/10.17341/gazimmfd.791935
  • Tumsekcali, E., Ayyildiz, E., & Taskin, A. (2021). Interval valued intuitionistic fuzzy AHP-WASPAS based public transportation service quality evaluation by a new extension of SERVQUAL Model: P-SERVQUAL 4.0. Expert Systems with Applications, 186, 115757. https://doi.org/10.1016/j.eswa.2021.115757
  • Yildiz, A., Ayyildiz, E., Taskin Gumus, A., & Ozkan, C. (2021). A framework to prioritize the public expectations from water treatment plants based on trapezoidal type-2 fuzzy AHP method. Environmental Management, 67(3), 439-448. https://doi.org/10.1007/s00267-020-01367-5
  • Yılmaz, Ö. F., Özçelik, G., & Yeni, F. B. (2021). Ensuring sustainability in the reverse supply chain in case of the ripple effect: A two-stage stochastic optimization model. Journal of Cleaner Production282, 124548. https://doi.org/10.1016/j.jclepro.2020.124548


  • Ayyildiz, E., & Taskin Gumus, A. (2020). A novel spherical fuzzy AHP-integrated spherical WASPAS methodology for petrol station location selection problem: a real case study for İstanbul. Environmental Science and Pollution Research, 27(29), 36109-36120. https://doi.org/10.1007/s11356-020-09640-0
  • Ayyildiz, E., Taskin Gumus, A., & Erkan, M. (2020). Individual credit ranking by an integrated interval type-2 trapezoidal fuzzy Electre methodology. Soft Computing, 24, 16149-16163. https://doi.org/10.1007/s00500-020-04929-1
  • Cevikcan, E., Aslan, D., & Yeni, F. B. (2020). Disassembly line design with multi-manned workstations: a novel heuristic optimisation approach. International Journal of Production Research, 58(3), 649-670. https://doi.org/10.1080/00207543.2019.1587190
  • Kaya, H., Kirmaci, V., & Es, H. A. (2020). Performance modeling of parallel-connected ranque-hilsch vortex tubes using a generalizable and robust ann. Heat Transfer Research, 51(15). https://doi.org/10.1615/heattransres.2020035587
  • Koç, Ç., Laporte, G., & Tükenmez, İ. (2020). A review of vehicle routing with simultaneous pickup and delivery. Computers & Operations Research122, 104987. https://doi.org/10.1016/j.cor.2020.104987
  • Singer, H., & Özşahin, Ş., (2020). A multiple criteria analysis of factors influencing surface roughness of wood and wood-based materials in the planning process. Cerne , vol.26, no.1, 58-65. http://doi.org/10.1590/01047760202026012659
  • Yildiz, A., Ayyildiz, E., Taskin Gumus, A., & Ozkan, C. (2020). A modified balanced scorecard-based hybrid pythagorean fuzzy AHP-TOPSIS methodology for ATM site selection problem. International Journal of Information Technology & Decision Making, 19(02), 365-384. https://doi.org/10.1142/S0219622020500017
  • Yılmaz, Ö. F., Özçelik, G., & Yeni, F. B. (2020). Lean holistic fuzzy methodology employing cross-functional worker teams for new product development projects: A real case study from high-tech industry. European Journal of Operational Research282(3), 989-1010. https://doi.org/10.1016/j.ejor.2019.09.048
  • Yılmaz, Ö. F., (2020). Attaining flexibility in seru production system by means of Shojinka: An optimization model and solution approaches. Computers & Operations Research, vol.119. 10.1016/j.cor.2020.104917
  • Yılmaz, Ö. F., (2020). Examining additive manufacturing in supply chain context through an optimization model. Computers & Industrial Engineering, vol.142. 10.1016/j.cie.2020.106335
  • Yılmaz, Ö. F., (2020). Operational strategies for seru production system: a bi-objective optimisation model and solution methods. International Journal Of Production Research, vol.58, no.11, 3195-3219. 10.1080/00207543.2019.1669841


  • Hamzaçebi, C., Es, H. A., & Çakmak, R. (2019). Forecasting of Turkey?s monthly electricity demand by seasonal artificial neural network. Neural Computing and Applications, 31, 2217-2231. https://doi.org/10.1007/s00521-017-3183-5
  • Kucukkoc, I., Buyukozkan, K., Satoğlu, Ş. I., & Zhang, D. Z., (2019). A mathematical model and artificial bee colony algorithm for the lexicographic bottleneck mixed-model assembly line balancing problem. Journal Of Intelligent Manufacturing, vol.30, no.8, 2913-2925. http://doi.org/10.1007/s10845-015-1150-5
  • Özşahin, Ş., Singer, H., Temiz, A., & Yildirim, İ., (2019). Selection of Softwood Species for Structural and Non-Structural Timber Construction by Using the Analytic Hierarchy Process (AHP) and the Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA). Baltic Forestry, vol.25, no.2, 281-288.
  • Varol, T., & Özşahin, Ş., (2019). Artificial neural network analysis of the effect of matrix size and milling time on the properties of flake Al-Cu-Mg alloy particles synthesized by ball milling. Particulate Science and Technology, vol.37, no.3, 381-390. http://doi.org/10.1080/02726351.2017.1381658
  • Yeni, F. B., & Özçelik, G. (2019). Interval-valued Atanassov intuitionistic Fuzzy CODAS method for multi criteria group decision making problems. Group Decision and Negotiation28, 433-452. https://doi.org/10.1007/s10726-018-9603-9
  • Yılmaz, Ö. F., & Durmuşoğlu, M. B., (2019). Multi-objective scheduling problem for hybrid manufacturing systems with walking workers. International Journal of Industrial Engineering-Theory Applications And Practice, vol.26, no.5, 625-650. https://doi.org/10.23055/ijietap.2019.26.5.2810


  • Es, H. A., Hamzacebi, C., & Firat, S. U. O. (2018). GRA-TRI: A Multicriteria Decision Aid Classification Method based on Grey Relational Analysis. Journal of grey system, 30(3). https://www.webofscience.com/wos/woscc/full-record/WOS:000440392500001
  • Özşahin, Ş., & Murat, M., (2018). Prediction of equilibrium moisture content and specific gravity of heat-treated wood by artificial neural networks. European Journal of Wood and Wood Products, vol.76, no.2, 563-572. http://doi.org/10.1007/s00107-017-1219-2
  • Singer, H., & Özşahin, Ş., (2018). Employing an analytic hierarchy process to prioritize factors influencing surface roughness of wood and wood-based materials in the sawing process. Turkish Journal of Agriculture and Forestry, vol.42, no.5, 364-371. http://doi.org/10.3906/tar-1801-138
  • Ünver, M., Özçelik, G., & Olgun, M. (2018). A fuzzy measure theoretical approach for multi criteria decision making problems containing sub-criteria. Journal of Intelligent & Fuzzy Systems, 35(6), 6461-6468. https://doi.org/10.3233/JIFS-18396
  • Varol, T., Çanakçi, A., & Özşahin, Ş., (2018). Prediction of effect of reinforcement content, flake size and flake time on the density and hardness of flake AA2024-SiC nanocomposites using neural networks. Journal of Alloys and Compounds, vol.739, 1005-1014. http://doi.org/10.1016/j.jallcom.2017.12.256
  • Varol, T., Çanakçi, A., Özşahin, Ş., Erdemir, F., & Özkaya, S., (2018). Artificial neural network-based prediction technique for coating thickness in Fe-Al coatings fabricated by mechanical milling. Particulate Science and Technology, vol.36, no.6, 742-750. http://doi.org/10.1080/02726351.2017.1301607
  • Yılmaz, Ö. F., & Durmuşoğlu, M. B., (2018). A performance comparison and evaluation of metaheuristics for a batch scheduling problem in a multi-hybrid cell manufacturing system with skilled workforce assignment. Journal of Industrial and Management Optimization, vol.14, no.3, 1219-1249.  10.3934/jimo.2018007


  • Akyüz, İ., Özşahin, Ş., Tiryaki, S., & Aydın, A., (2017). An application of artificial neural networks for modeling formaldehyde emission based on process parameters in particleboard manufacturing process. Clean Technologies and Environmental Policy, Vol.19, 1449-1458.
  • Öksüz, M. K., Büyüközkan, K., & Satoğlu, Ş. I., (2017). U-shaped assembly line worker assignment and balancing problem: A mathematical model and two meta-heuristics. Computers & Industrial Engineering, vol.112, 246-263. http://doi.org/10.1016/j.cie.2017.08.030
  • Seyhan, M., Akansu, Y. E., Murat, M., Korkmaz, Y., & Akansu, S. O., (2017). Performance prediction of PEM fuel cell with wavy serpentine flow channel by using artificial neural network. International Journal of Hydrogen Energy42(40), 25619-25629. https://doi.org/10.1016/j.ijhydene.2017.04.001
  • Tiryaki, S., Özşahin, Ş., & Aydin, A., (2017). Employing artificial neural networks for minimizing surface roughness and power consumption in abrasive machining of wood. European Journal of Wood and Wood Products, vol.75, no.3, 347-358. http://doi.org/10.1007/s00107-016-1050-1
  • Yilmaz, Ö. F., & Pardalos, P. M., (2017). Minimizing average lead time for the coordinated scheduling problem in a two-stage supply chain with multiple customers and multiple manufacturers. Computers & Industrial Engineering, vol.114, 244-257. 10.1016/j.cie.2017.10.018
  • Yilmaz, Ö. F., Öztayşi, B., Durmuşoğlu, M. B., & Öner, S. C., (2017). Determination of material handling equipment for lean in-plant logistics using fuzzy analytical network process considering risk attitudes of the experts. International Journal of Industrial Engineering-Theory Applications and Practice, vol.24, no.1, 81-122. https://doi.org/10.23055/ijietap.2017.24.1.2890


  • Buyukozkan, K., Kucukkoc, I., Satoğlu, Ş. I., & Zhang, D. Z., (2016). Lexicographic bottleneck mixed-model assembly line balancing problem: Artificial bee colony and tabu search approaches with optimised parameters. Expert Systems with Applications, vol.50, 151-166. http://doi.org/10.1016/j.eswa.2015.12.018
  • Tiryaki, S., Malkocoglu, A., & Özşahin, Ş., (2016). Artificial neural network modeling to predict optimum power consumption in wood machining. Drewno, vol.59, no.196, 109-125. http://doi.org/10.12841/wood.1644-3985.140.08
  • Yılmaz, Ö. F., Çevikcan, E., & Durmuşoğlu, M. B., (2016). Scheduling batches in multi hybrid cell manufacturing system considering worker resources: A case study from pipeline industry. Advances in Production Engineering & Management, vol.11, no.3, 192-206. 10.14743/apem2016.3.220


  • Çanakçi, A., Varol, T., & Özşahin, Ş., (2015). Artificial neural network to predict the effect of heat treatment, reinforcement size, and volume fraction on AlCuMg alloy matrix composite properties fabricated by stir casting method. International Journal of Advanced Manufacturing Technology, vol.78, 305-317. http://doi.org/10.1007/s00170-014-6646-1
  • Varol, T., Çanakçi, A., & Özşahin, Ş., (2015). Modeling of the Prediction of Densification Behavior of Powder Metallurgy Al-Cu-Mg/B4C Composites Using Artificial Neural Networks. Acta Metallurgica Sinica-English Letters, vol.28, no.2, 182-195. http://doi.org/10.1007/s40195-014-0184-6


  • Büyüközkan, K., & Sarucan, A., (2014). Applicability of artificial bee colony algorithm for nurse scheduling problems. International Journal of Computational Intelligence Systems, vol.7, 121-136. http://doi.org/10.1080/18756891.2014.853957
  • Çanakçi, A., Özşahin, Ş., & Varol, T., (2014). Prediction of effect of reinforcement size and volume fraction on the abrasive wear behavior of AA2014/B4Cp MMCs using artificial neural network. Arabian Journal for Science and Engineering, vol.39, no.8, 6351-6361. http://doi.org/10.1007/s13369-014-1157-9
  • Es, H. A., Kalender, F. Y., & Hamzaçebi, C. (2014). Forecasting the net energy demand of Turkey by artificial neural networks. Journal of the Faculty of Engineering and Architecture of Gazi University, 29(3). https://www.webofscience.com/wos/woscc/full-record/WOS:000343887100007
  • Hamzacebi, C., & Es, H. A. (2014). Forecasting the annual electricity consumption of Turkey using an optimized grey model. Energy, 70, 165-171. https://doi.org/10.1016/j.energy.2014.03.105
  • Malkocoglu, A., Yerlikaya, N. C., & Özşahin, Ş., (2014). Evalualuation and optimization of bending moment capacity of corner joints with different boring plans in cabinet construction. Wood Research, vol.59, no.1, 201-215.
  • Özşahin, Ş., & Aydin, İ., (2014). Prediction of the optimum veneer drying temperature for good bonding in plywood manufacturing by means of artificial neural network. Wood Science and Technology, vol.48, no.1, 59-70. http://doi.org/10.1007/s00226-013-0583-2
  • Tiryaki, S., Malkoçoğlu, A., & Özşahin, Ş. (2014). Using artificial neural networks for modeling surface roughness of wood in machining process. Construction and Building Materials66, 329-335. https://doi.org/10.1016/j.conbuildmat.2014.05.098
  • Tiryaki, S., Özşahin, Ş., & Yildirim, İ., (2014). Comparison of artificial neural network and multiple linear regression models to predict optimum bonding strength of heat-treated woods. International Journal of Adhesion and Adhesives, vol.55, 29-36. http://doi.org/10.1016/j.ijadhadh.2014.07.005
  • Varol, T., Çanakçi, A., & Özşahin, Ş., (2014). Prediction of the influence of processing parameters on synthesis of Al2024-B4C composite powders in a planetary mill using an artificial neural network. Science And Engineering of Composite Materials, vol.21, no.3, 411-420. http://doi.org/10.1515/secm-2013-0148
  • Yildirim, İ., Özşahin, Ş., & Okan, O. T., (2014). Prediction of non-wood forest products trade using artificial neural networks. Journal of Agricultural Science and Technology, vol.16, 1493-1504. 


  • Çanakçi, A., Varol, T., & Özşahin, Ş., (2013). Analysis of the effect of a new process control agent technique on the mechanical milling process using a neural network model: Measurement and modeling. Measurement, vol.46, no.6, 1818-1827. http://doi.org/10.1016/j.measurement.2013.02.005
  • Çanakçi, A., Varol, T., & Özşahin, Ş., (2013). Prediction of effect of volume fraction, compact pressure and milling time on properties of Al-Al2O3 MMCs using neural networks. Metals And Materials International, vol.19, no.3, 519-526. http://doi.org/10.1007/s12540-013-3021-y
  • Demirkir, C., Özşahin, Ş., Aydin, İ., & Çolakoğlu, G., (2013). Optimization of some panel manufacturing parameters for the best bonding strength of plywood. International Journal of Adhesion and Adhesives, vol.46, 14-20. http://doi.org/10.1016/j.ijadhadh.2013.05.007
  • Özsahin, S., (2013). Optimization of process parameters in oriented strand board manufacturing with artificial neural network analysis. European Journal of Wood and Wood Products, vol.71, no.6, 769-777. http://doi.org/10.1007/s00107-013-0737-9
  • Varol, T., Çanakçi, A., & Özşahin, Ş., (2013). Artificial neural network modeling to effect of reinforcement properties on the physical and mechanical properties of Al2024-B4C composites produced by powder metallurgy. Composites Part B-Engineering, vol.54, 224-233. http://doi.org/10.1016/j.compositesb.2013.05.015


  • Çanakçi, A., Özşahin, Ş., & Varol, T., (2012). Modeling the influence of a process control agent on the properties of metal matrix composite powders using artificial neural networks. Powder Technology, vol.228, 26-35. http://doi.org/10.1016/j.powtec.2012.04.045
  • Ozsahin, S., (2012). The use of an artificial neural network for modeling the moisture absorption and thickness swelling of oriented strand board. Bioresources, vol.7, no.1, 1053-1067.


  • Yildirim, İ., Özşahin, Ş., & Akyüz, K. C., (2011). Prediction of the financial return of the paper sector with artificial neural networks. Bioresources, vol.6, no.4, 4076-4091.