Comparison of artificial neural networks learning methods to evaluate supply chain performance
Abdi-Khanghah, M., Bemani, A., Naserzadeh, Z. & Zhang, Z. (2018). Prediction of solubility of N-alkanes in supercritical CO2 using RBF-ANN and MLP-ANN. Journal of CO2 Utilization, 25, 108-119. https://doi.org/10.1016/j.jcou.2018.03.008.
Ahi, P., & Searcy, C. (2015). Assessing sustainability in the supply chain: A triple bottom line approach.
Akkawuttiwanich, P., & Yenradee, P. (2018). Fuzzy QFD approach for managing SCOR performance indicators.
Bertrand, J. W. M., & Fransoo, J. (2002). Operations management research methodologies using quantitative modeling.
Bilgehan, M. (2011). Comparison of ANFIS and NN models – With a study in critical buckling load estimation.
Brandenburg, M., Govindan, K., Sarkis, J., & Seuring, S. (2014). Quantitative models for managing supply chain risks.
Bukhori, I. B., Widodo, K. H., & Ismoyowati, D. (2015). Evaluation of Poultry Supply Chain Performance in XYZ Slaughtering House Yogyakarta using SCOR and AHP Method.
Clivillé, V., & Berrah, L. (2012). Overall performance measurement in a supply chain: towards a supplier-prime manufacturer based model.
Dias, L. S., & Ierapetritou, M. G. (2017). From process control to supply chain management: an overview of integrated decision making strategies.
Didehkhani, H., Jassbi, J., & Pilevari, N. (2009). Assessing flexibility in supply chain using adaptive neuro fuzzy inference system. In
Dissanayake, C. K., & Cross, J. A. (2018). Systematic mechanism for identifying the relative impact of supply chain performance areas on the overall supply chain performance using SCOR Model and SEM.
Estampe, D., Lamouri, S., Paris, J., & Brahim-Djelloul, S. (2013). A framework for analysing supply chain performance evaluation models.
Fan, X., Zhang, S., Wang, L., Yang, Y., & Hapeshi, K. (2013). An evaluation model of supply chain performances using 5DBSC and LMBP Neural Network Algorithm.
Ganga, G. M. D., & Carpinetti, L. C. R. (2011). A fuzzy logic approach to supply chain performance management.
Golparvar, M., & Seifbarghy, M. (2009). Application of SCOR Model in an Oil- producing Company.
Gunasekaran, A., Patel, C., & Tirtiroglu, E. (2001). Performance measures and metrics in a supply chain environment.
Jalalvand, F., Teimoury, E., Makui, A., Aryanezhad, M. B., & Jolai, F. (2011). A method to compare supply chains of an industry.
Kocaoğlu, B., Gülsün, B., & Tanyaş, M. (2013). A SCOR based approach for measuring a benchmarkable supply chain performance.
Kurtgoz, Y., Karagoz, M., & Deniz, E. (2017). Biogas engine performance estimation using ANN.
Lima-Junior, F. R., & Carpinetti, L. C. R (2019). Predicting supply chain performance based on SCOR® metrics and multilayer perceptron neural networks.
Lima-Junior, F. R., & Carpinetti, L. C. R. (2017). Quantitative models for supply chain performance evaluation: A literature review.
Liu, F. F., & Liu, Y. C. (2017). A methodology to assess the supply chain performance based on virtual-gap measures.
Maestrini, V., Luzzini, D., Maccarrone, P., & Caniato, F. (2017). Supply chain performance measurement systems: A systematic review and research agenda.
Marchand, D., & Raymond, L. (2008). Researching performance measurement systems – An information system perspective.
Maroufpoor, S., Shiri, J., & Maroufpoor, E. (2019). Modeling the sprinkler water distribution uniformity by data-driven methods based on effective variables.
Mentzer, J. T., DeWitt, W., Keebler, J. S., Min, S., Nix, N. W., Smith, C. D., & Zacharia, Z. G. (2001). Defining supply chain management.
Moharamkhani, A., Amiri, A. B., & Mina, H (2017). Supply chain performance measurement using SCOR model based on interval-valued fuzzy TOPSIS.
Montgomery, D. C., & Runger, G. C. (2009).
Mukherjee, I., & Routroy, S. (2012). Comparing the performance of neural networks developed by using Levenberg–Marquardt and Quasi-Newton with the gradient descent algorithm for modelling a multiple response grinding process.
Patuwo, E., Hu, M. Y., & Hung, M. S. (1993). Two-group classification using neural networks.
Rezaee, M., Jozmaleki, M., & Valipour, M. (2018). Integrating dynamic fuzzy C-means, data envelopment analysis and artificial neural network to online prediction performance of companies in stock exchange.
Sellitto, M. A., Pereira, G. M., Borchardt, M., Silva, R., & Viegas, C. V. (2015). A SCOR-based model for supply chain performance measurement: application in the footwear industry.
Shafiee, M., Lotfi, F. H., & Saleh, H. (2014). Supply chain performance evaluation with data envelopment analysis and balanced scorecard approach.
Silva, I. N., Spati, D. H., & Flauzino, R. A. (2010).
Supply Chain Council – SCC. (2012).
Tkác, M., & Verner, R. (2016). Artificial neural networks in business: two decades of research.
Tripathy, P. P., & Kumar, S. (2009). Neural network approach for food temperature prediction during solar drying.
Yang, J., & Jiang, H. (2012). Fuzzy evaluation on supply chains’ overall performance based on AHM and M(1,2,3).