Search for Author, Title, Keyword
RESEARCH PAPER
An artificial neural network model supported with multi criteria decision making approaches for maintenance planning in hydroelectric power plants
 
More details
Hide details
1
Industrial Engineering Department Kırıkkale University 71451, Kırıkkale, Turkey
 
 
Publication date: 2020-09-30
 
 
Eksploatacja i Niezawodność – Maintenance and Reliability 2020;22(3):400-418
 
KEYWORDS
ABSTRACT
Power plants are the large-scale production facilities with the main purpose of realizing uninterrupted, reliable, efficient, economic and environmentally friendly energy generation. Maintenance is one of the critical factors in achieving these comprehensive goals, which are called as sustainable energy supply. The maintenance processes carried out in order to ensure sustainable energy supply in the power plants should be managed due to the costs arising from time requirement, the use of material and labor, and the loss of generation. In this respect, it is critical that the fault dates are forecasted, and maintenance is performed without failure in power plants consisting of thousands of equipment. In this context in this study, the maintenance planning problem for equipment with high criticality level is handled in one of the large-scale hydroelectric power plants that meet the quintile of Turkey’s energy demand as of the end of 2018. In the first stage, the evaluation criteria determined by the power plant experts are weighted by the Analytical Hierarchy Process (AHP), which is an accepted method in the literature, in order to determine the criticality levels of the equipment in terms of power plant at the next stage. In order to obtain the final priority ranking of the equipment in terms of power plant within the scope of these weights, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used because of its advantages compared to other outranking algorithms. As a result of this solution, for the 14 main equipment groups with the highest criticality level determined on the basis of the power plant, periods between two breakdowns are estimated, and maintenance planning is performed based on these periods. In the estimation phase, an artificial neural network (ANN) model has been established by using 11-years fault data for selected equipment groups and the probable fault dates are estimated by considering a production facility as a system without considering the sector for the first time in the literature. With the plan including the maintenance activities that will be carried out before the determined breakdown dates, increasing the generation efficiency, extending the economic life of the power plant, minimizing the generation costs, maximizing the plant availability rate and maximizing profit are aimed. The maintenance plan is implemented for 2 years in the power plant and the unit shutdowns resulting from the selected equipment groups are not met and the mentioned goals are reached.
 
REFERENCES (92)
1.
Adoghe AU, Awosope COA, Ekeh JC. Asset maintenance planning in electric power distribution network using statistical analysis of outage data. International Journal of Electrical Power & Energy Systems 2013; 47: 424-35,https://doi.org/10.1016/j.ijep....
 
2.
Adya M, Collopy F. How effective are neural networks at forecasting and prediction? A review and evaluation. Journal of Forecasting 1998;17: 481-95, https://doi.org/10.1002/(SICI)...<481::AID-FOR709>3.0.CO;2-Q.
 
3.
Al-Shayea QK. Artificial neural networks in medical diagnosis. International Journal of Computer Science Issues 2011; 8: 150-154.
 
4.
Aly MF, El-hameed HMA. Integrating AHP and genetic algorithm model adopted for personal selection. International Journal of Engineering Trends and Technology (IJETT) 2013; 6.
 
5.
Arıbaş M, Özcan U. Akademik araştırma projelerinin AHP ve TOPSIS yöntemleri kullanılarak değerlendirilmesi. Politeknik Dergisi 2016;19: 163-173.
 
6.
Ayodeji A, Liu Y kuo, Xia H. Knowledge base operator support system for nuclear power plant fault diagnosis. Progress in Nuclear Energy 2018; 105: 42-50, https://doi.org/10.1016/j.pnuc....
 
7.
Bangalore P, Patriksson M. Analysis of SCADA data for early fault detection, with application to the maintenance management of wind turbines. Renewable Energy 2018; 115: 521-532, https://doi.org/10.1016/j.rene....
 
8.
Barros CP, Wanke P. An analysis of African airlines efficiency with two-stage TOPSIS and neural networks. Journal of Air Transport Management 2015; 44-45: 90-102, https://doi.org/10.1016/j.jair....
 
9.
Behzadian M, Kazemzadeh RB, Albadvi A, Aghdasi M. PROMETHEE: A comprehensive literature review on methodologies and applications.European Journal of Operational Research 2010; 200: 198-215, https://doi.org/10.1016/j.ejor....
 
10.
Behzadian M, Khanmohammadi Otaghsara S, Yazdani M, Ignatius J. A state-of the-art survey of TOPSIS applications. Expert Systems with Applications 2012; 39: 13051-13069, https://doi.org/10.1016/j.eswa....
 
11.
Bevilacqua M, Braglia M. Analytic hierarchy process applied to maintenance strategy selection. Reliability Engineering & System Safety 2000; 70: 71-83, https://doi.org/10.1016/S0951-....
 
12.
Bi R, Zhou C, Hepburn DM. Detection and classification of faults in pitch-regulated wind turbine generators using normal behaviour models based on performance curves. Renewable Energy 2017; 105: 674-688, https://doi.org/10.1016/j.rene....
 
13.
Box GEP, Jenkins GM, Reinsel GC, Ljung GM. Time series analysis: forecasting and control. John Wiley & Sons; 2015.
 
14.
Braglia M, Carmignani G, Frosolini M, Zammori F. Data classification and MTBF prediction with a multivariate analysis approach. Reliability Engineering & System Safety 2012; 97: 27-35, https://doi.org/10.1016/j.ress....
 
15.
Chang P-L, Chen Y-C. A fuzzy multi-criteria decision making method for technology transfer strategy selection in biotechnology. Fuzzy Sets and Systems 1994; 63: 131-139, https://doi.org/10.1016/0165-0....
 
16.
Chen B, Matthews PC, Tavner PJ. Wind turbine pitch faults prognosis using a-priori knowledge-based ANFIS. Expert Systems with Applications 2013; 40: 6863-6876, https://doi.org/10.1016/j.eswa....
 
17.
Chen D, Fang Z, Wu L, Zhu X. Study on the Prediction of Complex Equipment El MTBF DGMW (p/q)(1, 1) Model Based on the Small Sample. Journal of Grey System 2013; 25.
 
18.
Cheng B, Titterington DM. Neural networks: A review from a statistical perspective. Statistical Science 1994: 2-30, https://doi.org/10.1214/ss/117....
 
19.
Çamkoru AM, Sayın VO. Bakım maliyeti yönetimi, Mühendis ve Makine 2012; 53(635): 16-20.
 
20.
Dougherty M. A review of neural networks applied to transport. Transportation Research Part C: Emerging Technologies 1995; 3: 247-260,https://doi.org/10.1016/0968-0....
 
21.
Energy statistics. UCTEA. The Chamber of Electrical Engineers (www.emo.org.tr/ekler/5aa1f79e3852d94_ek.pdf).
 
22.
Fernandez C, Soria E, Martin JD, Serrano AJ. Neural networks for animal science applications: Two case studies. Expert Systems with Applications 2006; 31:444-450, https://doi.org/10.1016/j.eswa....
 
23.
Gardner MW, Dorling SR. Artificial neural networks (the multilayer perceptron)-a review of applications in the atmospheric sciences. Atmospheric Environment 1998; 32: 2627-2636, https://doi.org/10.1016/S1352-....
 
24.
Govindan K, Jepsen MB. ELECTRE: A comprehensive literature review on methodologies and applications. European Journal of Operational Research 2016; 250: 1-29, https://doi.org/10.1016/j.ejor....
 
25.
Granger CWJ, Terasvirta T. Modelling non-linear economic relationships. OUP Cat 1993.
 
26.
Gul M, Celik E, Aydin N, Taskin Gumus A, Guneri AF. A state of the art literature review of VIKOR and its fuzzy extensions on applications. Applied Soft Computing 2016; 46: 60-89, https://doi.org/10.1016/j.asoc....
 
27.
Hajian A, Styles P. Application of soft computing and intelligent methods in geophysics. Springer International Publishing 2018, https://doi.org/10.1007/978-3-....
 
28.
Haykin S. Neural networks: a comprehensive foundation. Prentice Hall PTR; 1994.
 
29.
Hebb DO. The Organizations of Behavior: a Neuropsychological Theory. Lawrence Erlbaum; 1963.
 
30.
Hebbs DG. The organization of behavior. Wiely Sons, New York, NY, USA 1949.
 
31.
Hwang C-L, Yoon K. Methods for multiple attribute decision making. Multiple Attribute Decision Making 1981: 58-191, https://doi.org/10.1007/978-3-....
 
32.
Illias HA, Chai XR, Bakar AHA, Mokhlis H. Transformer incipient fault prediction using combined artificial neural network and various particle swarm optimisation techniques. PLoS One 2015; 10: 1-17, https://doi.org/10.1371/journa....
 
33.
Ishizaka A, Labib A. Review of the main developments in the analytic hierarchy process. Expert Systems with Applications 2011; 38: 14336-14345, https://doi.org/10.1016/j.eswa....
 
34.
Jiang R, Huang R, Huang C. Modeling the effect of environmental conditions on reliability of wind turbines. Journal of Shanghai Jiaotong University (Science) 2016; 21: 462-466, https://doi.org/10.1007/s12204....
 
35.
Jones B, Jenkinson I, Yang Z, Wang J. The use of Bayesian network modelling for maintenance planning in a manufacturing industry. Reliability Engineering & System Safety 2010; 95: 267-277, https://doi.org/10.1016/j.ress....
 
36.
Kahraman C, Ruan D, Doǧan I. Fuzzy group decision-making for facility location selection. Information Sciences 2003; 157: 135-153,https://doi.org/10.1016/S0020-....
 
37.
Kalogirou SA. Artificial neural networks in renewable energy systems applications: a review. Renewable and Sustainable Energy Reviews 2001; 5: 373-401, https://doi.org/10.1016/S1364-....
 
38.
Komal S, Sharma SP. Fuzzy reliability analysis of repairable industrial systems using soft-computing based hybridized techniques. Applied Soft Computing 2014; 24: 264-276, https://doi.org/10.1016/j.asoc....
 
39.
Kubler S, Robert J, Derigent W, Voisin A, Le Traon Y. A state-of the-art survey & testbed of fuzzy AHP (FAHP) applications. Expert Systems with Applications 2016; 65: 398-422, https://doi.org/10.1016/j.eswa....
 
40.
Kumar A, Sah B, Singh AR, Deng Y, He X, Kumar P, et al. A review of multi criteria decision making (MCDM) towards sustainable renewable energy development. Renewable and Sustainable Energy Reviews 2017; 69: 596-609, https://doi.org/10.1016/j.rser....
 
41.
Kumar R, Singal SK. Penstock material selection in small hydropower plants using MADM methods. Renewable and Sustainable Energy Reviews 2015; 52: 240-255, https://doi.org/10.1016/j.rser....
 
42.
Kuo RJ, Chi SC, Kao SS. A decision support system for selecting convenience store location through integration of fuzzy AHP and artificial neural network. Computers in Industry 2002; 47: 199-214, https://doi.org/10.1016/S0166-....
 
43.
Kusiak A, Li W. The prediction and diagnosis of wind turbine faults. Renewable Energy 2011; 36:16-23, https://doi.org/10.1016/j.rene....
 
44.
Lee JW, Kim SH. Using analytic network process and goal programming for interdependent information system project selection. Computers & Operations Research 2000; 27: 367-382, https://doi.org/10.1016/S0305-....
 
45.
Liang R-H, Hsu Y-Y. Scheduling of hydroelectric generations using artificial neural networks. IEE Proceedings-Generation, Transmission and Distribution 1994;141: 452-458, https://doi.org/10.1049/ip-gtd....
 
46.
Liao S-H, Wen C-H. Artificial neural networks classification and clustering of methodologies and applications-literature analysis from 1995 to 2005 Expert Systems with Applications 2007; 32: 1-11, https://doi.org/10.1016/j.eswa....
 
47.
Liberatore MJ, Nydick RL. The analytic hierarchy process in medical and health care decision making: A literature review. European Journal of Operational Research 2008; 189: 194-207, https://doi.org/10.1016/j.ejor....
 
48.
Liu Y, Fan J, Li Y. One system reliability assessment method for CNC grynder. Eksploatacja i Niezawodnosc - Maintenancen and Reliability 2014; 16(1): 97-104.
 
49.
Lu Y, Sun L, Zhang X, Feng F, Kang J, Fu G. Condition based maintenance optimization for offshore wind turbine considering opportunities based on neural network approach. Applied Ocean Research 2018; 74: 69-79, https://doi.org/10.1016/j.apor....
 
50.
Mardani A, Zavadskas EK, Khalifah Z, Zakuan N, Jusoh A, Nor KM, et al. A review of multi-criteria decision-making applications to solve energy management problems: Two decades from 1995 to 2015. Renewable and Sustainable Energy Reviews 2017; 71: 216-256, https://doi.org/10.1016/j.rser....
 
51.
Mayadevi N, Vinodchandra S S, Ushakumari S. A review on expert system applications in power plants. International Journal of Electrical and Computer Engineering 2014; 4(1): 116.
 
52.
McCulloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 1943; 5: 115-133, https://doi.org/10.1007/BF0247....
 
53.
Messai A, Mellit A, Abdellani I, Massi Pavan A. On-line fault detection of a fuel rod temperature measurement sensor in a nuclear reactor core using ANNs. Progress in Nuclear Energy 2015; 79: 8-21, https://doi.org/10.1016/j.pnuc....
 
54.
Minsky M, Paper S.Perceptrons. MIT Press, Cambridge MA, USA 1969.
 
55.
Mohd Ali J, Hussain MA, Tade MO, Zhang J. Artificial Intelligence techniques applied as estimator in chemical process systems - A literature survey. Expert Systems with Applications 2015; 42: 5915-5931, https://doi.org/10.1016/j.eswa....
 
56.
Nasiri S, Khosravani MR, Weinberg K. Fracture mechanics and mechanical fault detection by artificial intelligence methods: A review. Engineering Failure Analysis 2017; 81: 270-293, https://doi.org/10.1016/j.engf....
 
57.
Polo FAO, Ferrero Bermejo J, Gómez Fernández JF, Crespo Márquez A. Failure mode prediction and energy forecasting of PV plants to assist dynamic maintenance tasks by ANN based models. Renewable Energy 2015;81: 227-238, https://doi.org/10.1016/j.rene....
 
58.
Özcan EC, Ünlüsoy S, Eren T. A combined goal programming - AHP approach supported with TOPSIS for maintenance strategy selection in hydroelectric power plants. Renewable and Sustainable Energy Reviews 2017;78: 1410-1423, https://doi.org/10.1016/j.rser....
 
59.
Pankratz A. Forecasting with univariate Box-Jenkins models: Concepts and cases. vol. 224. John Wiley & Sons; 2009.
 
60.
Ramadevi R, Sheela Rani B, Prakash V. Role of hidden neurons in an elman recurrent neural network in classification of cavitation signals. Int J Comput Appl 2012; 37(7): 9-13, https://doi.org/10.5120/4618-6....
 
61.
Ravì D, Wong C, Deligianni F, Berthelot M, Andreu-Perez J, Lo B, Yang GZ. Deep learning for health informatics. IEEE journal of biomedical and health informatics 2016; 21(1): 4-21, https://doi.org/10.1109/JBHI.2....
 
62.
Ripley BD. Statistical aspects of neural networks. Networks and Chaos-Statistical and Probabilistic Aspects 1993; 50: 40-123, https://doi.org/10.1007/978-1-....
 
63.
Rosenblatt F. The perceptron: a probabilistic model for information storage and organization in the brain. Psychological Review 1958; 65:386, https://doi.org/10.1037/h00425....
 
64.
Rumelhart DE, Hinton GE, Williams RJ. Learning internal representation by backpropagating errors. DE Rumelhart, & JL McCleland Parallel Distributed Processing: Explorations in the Microstructure of Cognition 1986; 1,https://doi.org/10.7551/mitpre....
 
65.
Saaty TL. The analytic hierarchy process: planning, priority setting, resource allocation. McGraw-Hill International Book Company; 1980.
 
66.
San Cristóbal JR. Multi-criteria decision-making in the selection of a renewable energy project in spain: The Vikor method. Renewable Energy 2011; 36: 498-502, https://doi.org/10.1016/j.rene....
 
67.
Schlechtingen M, Ferreira Santos I. Comparative analysis of neural network and regression based condition monitoring approaches for wind turbine fault detection. Mechanical Systems and Signal Processing 2011; 25:1849-1875, https://doi.org/10.1016/j.ymss....
 
68.
Schlechtingen M, Santos IF. Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 2: Application examples. Applied Soft Computing 2014; 14: 447-460, https://doi.org/10.1016/j.asoc....
 
69.
Shankaracharya DO, Samanta S, Vidyarthi AS. Computational intelligence in early diabetes diagnosis: A review. The Review of Diabetic Studies: RDS 2010; 7: 252-261, https://doi.org/10.1900/RDS.20....
 
70.
Sharda R. Neural networks for the MS/OR analyst: An application bibliography. Interfaces (Providence) 1994; 24: 116-130, https://doi.org/10.1287/inte.2....
 
71.
Sheela K G. Deepa S. N. Review on methods to fix number of hidden neurons in neural networks. Mathematical Problems in Engineering 2013, https://doi.org/10.1155/2013/4....
 
72.
Sindhu S, Nehra V, Luthra S. Investigation of feasibility study of solar farms deployment using hybrid AHP-TOPSIS analysis: Case study of India. Renewable and Sustainable Energy Reviews 2017; 73: 496-511, https://doi.org/10.1016/j.rser....
 
73.
Sipahi S, Timor M. The analytic hierarchy process and analytic network process: an overview of applications. Management Decision 2010; 48: 775-808, https://doi.org/10.1108/002517....
 
74.
Suganthi L, Samuel AA. Energy models for demand forecasting - A review. Renewable and Sustainable Energy Reviews 2012; 16:1223-1240, https://doi.org/10.1016/j.rser....
 
75.
Sun P, Li J, Wang C, Lei X. A generalized model for wind turbine anomaly identification based on SCADA data. Applied Energy 2016; 168:550-567, https://doi.org/10.1016/j.apen....
 
76.
Taha Z, Rostam S. A fuzzy AHP-ANN-based decision support system for machine tool selection in a flexible manufacturing cell. The International Journal of Advanced Manufacturing Technology 2011; 57: 719-733, https://doi.org/10.1007/s00170....
 
77.
Vaidya OS, Kumar S. Analytic hierarchy process: An overview of applications. European Journal of Operational Research 2006; 169: 1-29, https://doi.org/10.1016/j.ejor....
 
78.
Vedachalam N, Ramadass GA. Reliability assessment of multi-megawatt capacity offshore dynamic positioning systems. Applied Ocean Research 2017; 63: 251-261, https://doi.org/10.1016/j.apor....
 
79.
Velasquez M, Hester PT. An analysis of multi-criteria decision making methods. International Journal of Operations Research 2013;10: 56-66.
 
80.
Voyant C, Notton G, Kalogirou S, Nivet M-L, Paoli C, Motte F, et al. Machine learning methods for solar radiation forecasting: A review. Renewable Energy 2017; 105: 569-582, https://doi.org/10.1016/j.rene....
 
81.
Wang G, Huang SH, Dismukes JP. Product-driven supply chain selection using integrated multi-criteria decision-making methodology. International Journal of Production Economics 2004; 91: 1-15, https://doi.org/10.1016/S0925-....
 
82.
Wang JJ, Jing YY, Zhang CF, Zhao JH. Review on multi-criteria decision analysis aid in sustainable energy decision-making. Renewable and Sustainable Energy Reviews 2009; 13: 2263-2278, https://doi.org/10.1016/j.rser....
 
83.
Wang Z, Srinivasan RS. A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models. Renewable and Sustainable Energy Reviews 2017; 75: 796-808, https://doi.org/10.1016/j.rser....
 
84.
Wanke P, Azad MAK, Barros CP, Hassan MK. Predicting efficiency in Islamic banks: An integrated multicriteria decision making (MCDM) approach. Journal of International Financial Markets, Institutions and Money 2016; 45: 126-141, https://doi.org/10.1016/j. intfin.2016.07.004.
 
85.
Weron R. Electricity price forecasting: A review of the state-of-the-art with a look into the future. International Journal of Forecasting 2014; 30: 1030-1081, https://doi.org/10.1016/j.ijfo....
 
86.
White H. Learning in artificial neural networks: A statistical perspective. Neural Computation 1989; 1: 425-464, https://doi.org/10.1162/neco.1....
 
87.
Wong BK, Selvi Y. Neural network applications in finance: a review and analysis of literature (1990-1996). Information & Management 1998; 34:129-139, https://doi.org/10.1016/S0378-....
 
88.
Yang Y, Lu Z, Luo X, Ge Z, Qian Y. Mean failure mass and mean failure repair time: parameters linking reliability, maintainability and supportability. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2014;16(2):307-312.
 
89.
Zhang G, Patuwo BE, Hu MY. Forecasting with artificial neural networks:: The state of the art. International Journal of Forecasting 1998;14: 35-62, https://doi.org/10.1016/S0169-....
 
90.
Zhang GP. Neural networks for classification: a survey. IEEE Transactions on Systems, Man, and Cybernetics, Cybern Part C (Applications and Reviews) 2000; 30: 451-462, https://doi.org/10.1109/5326.8....
 
91.
Zhang Y, Ding X, Liu Y, Griffin PJ. An artificial neural network approach to transformer fault diagnosis. IEEE Transactions on Power Delivery 1996; 16: 55, https://doi.org/10.1109/MPER.1....
 
92.
Zyoud SH, Fuchs-Hanusch D. A bibliometric-based survey on AHP and TOPSIS techniques. Expert Systems with Applications 2017; 78:158-181, https://doi.org/10.1016/j.eswa....
 
 
CITATIONS (31):
1.
A novel approach to optimize the maintenance strategies: a case in the hydroelectric power plant
Evrencan Özcan, Rabia Yumuşak, Tamer Eren
Eksploatacja i Niezawodnosc - Maintenance and Reliability
 
2.
Artificial neural network-based decision support systems in manufacturing processes: A systematic literature review
Fredrick Mumali
Computers & Industrial Engineering
 
3.
An Artificial Neural Network Model for Maintenance Planning of Metro Trains
Muhammed GENÇER, Rabia YUMUŞAK, Evrencan ÖZCAN, Tamer EREN
Journal of Polytechnic
 
4.
Data Analysis of Related Factors of Adolescent Physical Exercise Behavior Based upon Artificial Neural Network Model
Zhiling Chen, Xinghong Dai, Lili Ren, Mohammad Hashmi
Wireless Communications and Mobile Computing
 
5.
Obezite Hastaları İçin Giyilebilir Teknolojilerin ÇKKV Yöntemleri İle Seçimi
Beyza AKINCI, Tuğba DANIŞAN, Tamer EREN
Journal of Polytechnic
 
6.
Asset management and maintenance programming for power distribution systems: A review
Mina Mirhosseini, Farshid Keynia
IET Generation, Transmission & Distribution
 
7.
Optimization of a Nature-Inspired Shape for a Vertical Axis Wind Turbine through a Numerical Model and an Artificial Neural Network
Damota Blanco, García Rodríguez, Casanova Couce, Miranda Telmo, Claudio Caccia, María Galdo
Applied Sciences
 
8.
An artificial neural network supported performance degradation modeling for electro-hydrostatic actuator
Songlin Nie, Jianhang Gao, Zhonghai Ma, Fanglong Yin, Hui Ji
Journal of the Brazilian Society of Mechanical Sciences and Engineering
 
9.
Comparison and selection of patient follow-up systems for covid-19 pandemic patients
Tamer Eren, Tuğba Danışan, Ayşegül Deringöz, Güler Aksüt
Fashion and Textiles
 
10.
Covid-19 Takibinde Giyilebilir Sağlık Teknolojilerinin ÇKKV Yöntemleri ile Değerlendirilmesi
Ayşegül DERİNGÖZ, Tuğba DANIŞAN, Tamer EREN
Journal of Polytechnic
 
11.
Availability Projections of Hydroelectric Power Plants through Monte Carlo Simulation
Marcos Oliveira, Patrícia Silva, Elisa Oliveira, André Marcato, Giovani Junqueira
Energies
 
12.
Dijital Pazarlama Strateji Seçimi: SWOT Analizi Ve Çok Ölçütlü Karar Verme Yöntemleri
Berk SAÇAN, Tamer EREN
Journal of Polytechnic
 
13.
Pandemi Sürecinde Sürdürülebilir Tedarik Zinciri Yönetimi için İlaç Deposu ve Aşı Dağıtım Merkezi Yeri Seçimi
Nursena ORAL, Selma YAPICI, Rabia YUMUŞAK, Tamer EREN
Journal of Polytechnic
 
14.
Internal Modifications to Optimize Pollution and Emissions of Internal Combustion Engines through Multiple-Criteria Decision-Making and Artificial Neural Networks
María Galdo, Javier Miranda, José Lorenzo, Claudio Caccia
International Journal of Environmental Research and Public Health
 
15.
Siber Güvenlik Uzmanın Çok Kriterli Karar Verme Yöntemleri ile Seçilmesi
Rabia YUMUŞAK, Tamer EREN
Uluslararası Yönetim Bilişim Sistemleri ve Bilgisayar Bilimleri Dergisi
 
16.
Power System Reliability and Maintenance Evolution: A Critical Review and Future Perspectives
Manuel Alvarez-Alvarado, Daniel Donaldson, Angel Recalde, Holguer Noriega, Zafar Khan, Washington Velasquez, Carlos Rodriguez-Gallegos
IEEE Access
 
17.
Pandemi Sürecinde KYK Yurtlarında Kalan Öğrenciler İçin Karantina Yeri Seçimi
Zeynep BİLEN, Merve YILDIZ, Beyza PEÇENEK, Tuğba DANIŞAN, Tamer EREN
Karadeniz Fen Bilimleri Dergisi
 
18.
DEPO YÖNETİMİNDE ENDÜSTRİ 4.0 UYGULAMASI: BİR İŞLETME İÇİN RFID TEKNOLOJİ SEÇİMİ
Ebru TAŞKIN, Nisanur GEZİK, Rabia YUMUŞAK, Tamer EREN
Endüstri Mühendisliği
 
19.
Encyclopedia of Data Science and Machine Learning
Rodriguez Lozano, Carlos Torres
 
20.
Hybrid Renewable Energy Resources Selection Based on Multi Criteria Decision Methods for Optimal Performance
Gama Ali, Hmeda Musbah, Hamed Aly, Timothy Little
IEEE Access
 
21.
Hasar Tespit Çalışmalarında Görevlendirilebilecek Dronların Bulanık Karar Verme Yöntemleri ile Değerlendirilmesi
Mert KARA, Tamer EREN
Journal of Polytechnic
 
22.
Acil Yardım Müdahalesi Yapan Birimler için Çok Ölçütlü Karar Verme Yöntemleri ile Kargo Drone Seçimi
Mert KARA, Rabia YUMUŞAK, Tamer EREN
Türkiye İnsansız Hava Araçları Dergisi
 
23.
Reimagining Multi-Criterion Decision Making by Data-Driven Methods Based on Machine Learning: A Literature Review
Huchang Liao, Yangpeipei He, Xueyao Wu, Zheng Wu, Romualdas Bausys
 
24.
Selection of Materials in Construction Industry with Multi-Criteria Decision Making Models
Weng Lam, Kah Liew, Weng Lam, D.A. Joshi, N.B. Ibrahim, D.M. Sangeetha
E3S Web of Conferences
 
25.
Reimagining multi-criterion decision making by data-driven methods based on machine learning: A literature review
Huchang Liao, Yangpeipei He, Xueyao Wu, Zheng Wu, Romualdas Bausys
Information Fusion
 
26.
Purpose-Driven Evaluation of Operation and Maintenance Efficiency and Safety Based on DIKWP
Yanfei Liu, Wentao Wang, Wenjun Wang, Chengbo Yu, Bowen Mao, Dongfang Shang, Yucong Duan
Sustainability
 
27.
BELEDİYE OTOBÜSLERİNİN YHT İSTASYONU İÇİN İLÇE GÜZERGÂHLARININ ÇKKV İLE BELİRLENMESİ: KIRIKKALE İLİ ÖRNEĞİ
Buse BAYRAM, Mert KARA, Rabia YUMUŞAK, Ahmet CÜREBAL, Tamer EREN
Konya Journal of Engineering Sciences
 
28.
Planning of prescriptive maintenance types for generator with fuzzy logic-based genetic algorithm in a hydroelectric power plant
Merve Bulut, Evrencan Özcan
Expert Systems with Applications
 
29.
Comparative evaluation of alternatives for management of wood wastes by using multi-criteria decision tools
Fatma Sayın, Gülay Topaloğlu, Bilge Ozbay, Ismail Ozbay
Environmental Research Communications
 
30.
AFAD Kentsel Arama Kurtarma Akreditasyonu İçin Personel Seçim Problemi
Tuğba DANIŞAN, Tamer EREN
Journal of Polytechnic
 
31.
Advances in Manufacturing IV
Patrycja Guzanek, Piotr Bawoł, Grzegorz Sobecki
 
eISSN:2956-3860
ISSN:1507-2711
Journals System - logo
Scroll to top