مرور نظام‌مند کاربردهای شبکه عصبی مصنوعی در مدیریت زنجیره تأمین
الموضوعات :سید مجتبی حسینی بامکان 1 , عارف طغرالجردی 2 , پوریا مالکی نژاد 3
1 - دانشگاه یزد
2 - دانشگاه یزد
3 - دانشگاه یزد
الکلمات المفتاحية: شبکه های عصبی, زنجیره تامین, انتخاب تامین کننده, طراحی لجستیک,
ملخص المقالة :
معیار موفقیت شرکت‌ها و سازمان‌ها در بازار رقابتی امروز عملکرد زنجیره تأمین شرکت هاست. به منظور بهبود عملکرد زنجیره تأمین شرکت‌ها و موفقیت آنها تاکنون از روش‌ها و تکنیک‌های مختلفی استفاده شده است. یکی از روش‌های پرکاربرد در زمنیه حل این معضلات، شبکه عصبی مصنوعی است. هدف از این پژوهش مرور نظام‌مند کاربردهای مختلف شبکه‌های عصبی مصنوعی در حل معظلات و مشکلات بخش‌های مختلف زنجیره تأمین است. بدین منظور در ابتدا با استفاده از مرور ادبیات، واژگان کلیدی ارتباطی میان این دو حوزه شناسایی گردید. با استفاده از کلمات کلیدی استخراج شده از ادبیات پژوهش اقدام به جستجو در میان دو پایگاه داده اسکوپوس و وب آف ساینس گردید. با جستجو در این پایگاه داده‌ها مقالات مرتبط به کاربرد شبکه عصبی مصنوعی در حوزه‌های مختلف زنجیره تإمین استخراج گردید. در نهایت مقالات با استفاده از ابزارهای متعدد فیلتر و سپس مقالات دارای اهمیت بالا شناسایی گردید. با استفاده از مقالات مهم شناسایی شده، دسته‌بندی‌های مختلفی از کاربردهای شبکه عصبی مصنوعی در مدیریت زنجیره تأمین صورت پذیرفت. نتایج این پژوهش نشان می‌دهد شبکه‌های عصبی مصنوعی در حل مسائل مربوط به مهندسی، علوم کامپیوتر و کسب و کار و مدیریت بیشترین کاربرد را داشته است.
Al-Mahasneh, A. J., Anavatti, S. G., & Garratt, M. A. (2018). Review of Applications of Generalized Regression Neural Networks in Identification and Control of Dynamic Systems. arXiv preprint arXiv:1805.11236.
Altiparmak, F., Gen, M., Lin, L., & Karaoglan, I. (2009). A steady-state genetic algorithm for multi-product supply chain network design. Computers & industrial engineering, 56(2), 521-537.
Azadeh, A., Sheikhalishahi, M., Neghab, A. P., & Asadzadeh, S. M. (2010). Modeling and Optimization of a Supply Chain Loop's Performance by an Integrated Neural Network-Fuzzy Regression-Ridge Regression Approach. Paper presented at the 2010 Fourth UKSim European Symposium on Computer Modeling and Simulation.
Benkachcha, S., Benhra, J., & El Hassani, H. (2008). Demand Forecasting in Supply Chain: Comparing Multiple Linear Regression and Artificial Neural Networks Approaches.
Bruzzone, A., & Orsoni, A. (2003). AI and simulation-based techniques for the assessment of supply chain logistic performance. Paper presented at the Proceedings of the 36th annual symposium on Simulation.
Cagliano, A. C., & Rafele, C. (2008). Simulation for Logistics Performance Management: Comparing Different Approaches. Global Logistics Management. Sustainability, Quality, Risks. Operations and Technology Management, 9, 423-442.
Chen, H. C., Wee, H. M., Wang, K.-J., & Hsieh, Y.-H. (2007). Using artificial neural network in multi-agent supply chain systems. Paper presented at the Third International Conference on Natural Computation (ICNC 2007).
Chiu, M., & Lin, G. (2004). Collaborative supply chain planning using the artificial neural network approach. Journal of Manufacturing Technology Management, 15(8), 787-796.
Co, H. C., & Boosarawongse, R. (2007). Forecasting Thailand’s rice export: Statistical techniques vs. artificial neural networks. Computers & industrial engineering, 53(4), 610-627.
Du Toit, D., & Vlok, P.-J. (2014). Supply chain management: A framework of understanding. South african journal of industrial engineering, 25(3), 25-38.
Goh, M., Lim, J. Y., & Meng, F. (2007). A stochastic model for risk management in global supply chain networks. European Journal of Operational Research, 182(1), 164-173.
Gumus, A. T., & Gunerı, A. F. A NEURAL NETWORK BASED DEMAND FORECASTING SYSTEM FOR TWO-ECHELON SUPPLY CHAINS.
Gumus, A. T., Guneri, A. F., & Keles, S. (2009). Supply chain network design using an integrated neuro-fuzzy and MILP approach: A comparative design study. Expert Systems with Applications, 36(10), 12570-12577.
Hachicha, W. (2011). A simulation metamodelling based neural networks for lot-sizing problem in MTO sector. International Journal of Simulation Modelling, 10(4), 191-203.
Hahn, G. J., & Kuhn, H. (2012). Value-based performance and risk management in supply chains: A robust optimization approach. International Journal of Production Economics, 139(1), 135-144.
Handfield, R., & Nichols Jr, E. (1999). Introduction to. Supply Chain Management, Prentice Hall, Englewood Cliffs, NJ.
He, W. (2013). An inventory controlled supply chain model based on improved BP neural network. Discrete Dynamics in Nature and Society, 2013.
Holimchayachoutikul, P., Payongyam, P., Murino, T., Sopadang, A., Savino, M., & Elpidio, R. (2010). Application of artificial neural networks for demand forecasting in supply chain of thai frozen chicken products export industry. Paper presented at the HMS 2010.
Huang, L. (2009). Modeling to Plan and Evaluate the E-Supply Chain Management Platform. Paper presented at the 2009 International Conference on Management of e-Commerce and e-Government.
Keane, R. H., Herbohn, J., & Slaughter, G. (2003). Theoretical background of supply chain management and potential supply chain of north Queensland timber industry. Paper presented at the Marketing of farm-grown timber in tropical North Queensland: conference proceedings.
Kuo, R. J., Wang, Y. C., & Tien, F. C. (2010). Integration of artificial neural network and MADA methods for green supplier selection. Journal of Cleaner Production, 18(12), 1161-1170.
Liouane, Z., Lemlouma, T., Roose, P., Weis, F., & Messaoud, H. (2016). An improved elman neural network for daily living activities recognition. Paper presented at the International Conference on Intelligent Systems Design and Applications.
Liu, H. (2015). Forecasting Model of Supply Chain Management Based on Neural Network. Paper presented at the 2015 International Conference on Automation, Mechanical Control and Computational Engineering.
Mehidi, S., Chakrabarty, N., & Mohiuddin, H. (2014). An Application of Artificial Neural Network (ANN) Process to Assess Risk in Cement Industries in Bangladesh. Ind Eng Manage, 3(4), 1-6.
Melnyk, S. A., Narasimhan, R., & DeCampos, H. A. (2014). Supply chain design: issues, challenges, frameworks and solutions. In: Taylor & Francis.
Mohanty, R., Sahoo, G., & Dasgupta, J. (2012). Identification of Risk Factors in Globally Outsourced Software Projects using Logistic Regression and ANN. Int. J Sup. Chain. Mgt, 1, 2-11.
Mosavi, M., Khishe, M., Hatam Khani, Y., & Shabani, M. (2017). Training radial basis function neural network using stochastic fractal search algorithm to classify sonar dataset. Iran J Electr Electron Eng, 13(1), 100-111.
Naslund, D., & Williamson, S. (2010). What is management in supply chain management?-a critical review of definitions, frameworks and terminology. Journal of Management Policy and Practice, 11(4), 11-28.
Nurjanni, K. P., Carvalho, M. S., & Costa, L. (2017). Green supply chain design: A mathematical modeling approach based on a multi-objective optimization model. International Journal of Production Economics, 183, 421-432.
Pablo, B. M. J., Piedad, C. C. H., & Santiago, S. V. (2016). Neural Networks and Genetic Algorithms Applied for Implementing the Management Model “Triple A” in a Supply Chain. Case: Collection Centers of Raw Milk in the Azuay Province. Paper presented at the MATEC Web of Conferences.
Que, Q., & Belkin, M. (2016). Back to the Future: Radial Basis Function Networks Revisited. Paper presented at the AISTATS.
Singhal, P., Agarwal, G., & Mittal, M. (2012). Modeling of Risk Management Strategies for SMEs Using ANN under Various Levels of Operational Risks. Paper presented at the Advanced Materials Research.
Sodhi, M. S. (2005). Managing demand risk in tactical supply chain planning for a global consumer electronics company. Production and Operations management, 14(1), 69-79.
Sweeney, E. (2009). Introduction to Supply Chain Management and Logistics in a Volatile Global Environment.
Sweeney, E., Grant, D. B., & Mangan, D. J. (2015). The implementation of supply chain management theory in practice: an empirical investigation. Supply Chain Management: An International Journal, 20(1), 56-70.
Tavana, M., Fallahpour, A., Di Caprio, D., & Santos-Arteaga, F. J. (2016). A hybrid intelligent fuzzy predictive model with simulation for supplier evaluation and selection. Expert Systems with Applications, 61, 129-144.
Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence‐informed management knowledge by means of systematic review. British journal of management, 14(3), 207-222.
Tsai, F.-M., & Huang, L. J. (2017). Using artificial neural networks to predict container flows between the major ports of Asia. International Journal of Production Research, 55(17), 5001-5010.
Wang, F., Lai, X., & Shi, N. (2011). A multi-objective optimization for green supply chain network design. Decision Support Systems, 51(2), 262-269.
Yilmaz, I., Erik, N. Y., & Kaynar, O. (2010). Different types of learning algorithms of artificial neural network (ANN) models for prediction of gross calorific value (GCV) of coals. Scientific Research and Essays, 5(16), 2242-2249.
Ying, Z., & Hanbin, X. (2010). Study on the model of demand forecasting based on artificial neural network. Paper presented at the 2010 Ninth International Symposium on Distributed Computing and Applications to Business, Engineering and Science.
Zhu, Y., Xie, C., Sun, B., Wang, G.-J., & Yan, X.-G. (2016). Predicting China’s SME credit risk in supply chain financing by logistic regression, artificial neural network and hybrid models. Sustainability, 8(5), 433.
