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 Engineering. https://doi.org/10.1177/09544089221111586
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 Management. https://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 Iranica. https://doi.org/10.24200/SCI.2024.60981.708
2024
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
2023
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 applications, 35(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 & Engineering, 18(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 Engineering, 181, 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
2022
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 Iranica. https://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 Applications, 203, 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
2021
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 Systems, 23(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 Research, 59(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 Forestry, 27(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 University, 36(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 Production, 282, 124548. https://doi.org/10.1016/j.jclepro.2020.124548
2020
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 Research, 122, 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 Research, 282(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
2019
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 Negotiation, 28, 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
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
2017
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 Energy, 42(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
2016
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
2015
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
2014
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
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 Materials, 66, 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.
2013
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
2012
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.
2011
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.
