Forecasting return products in an integrated forward/reverse supply chain utilizing an ANFIS
D. Thresh Kumar ; Hamed Soleimani ; Govindan Kannan
International Journal of Applied Mathematics and Computer Science, Tome 24 (2014), p. 669-682 / Harvested from The Polish Digital Mathematics Library

Interests in Closed-Loop Supply Chain (CLSC) issues are growing day by day within the academia, companies, and customers. Many papers discuss profitability or cost reduction impacts of remanufacturing, but a very important point is almost missing. Indeed, there is no guarantee about the amounts of return products even if we know a lot about demands of first products. This uncertainty is due to reasons such as companies' capabilities in collecting End-of-Life (EOL) products, customers' interests in returning (and current incentives), and other independent collectors. The aim of this paper is to deal with the important gap of the uncertainties of return products. Therefore, we discuss the forecasting method of return products which have their own open-loop supply chain. We develop an integrated two-phase methodology to cope with the closed-loop supply chain design and planning problem. In the first phase, an Adaptive Network Based Fuzzy Inference System (ANFIS) is presented to handle the uncertainties of the amounts of return product and to determine the forecasted return rates. In the second phase, and based on the results of the first one, the proposed multi-echelon, multi-product, multi-period, closed-loop supply chain network is optimized. The second-phase optimization is undertaken based on using general exact solvers in order to achieve the global optimum. Finally, the performance of the proposed forecasting method is evaluated in 25 periods using a numerical example, which contains a pattern in the returning of products. The results reveal acceptable performance of the proposed two-phase optimization method. Based on them, such forecasting approaches can be applied to real-case CLSC problems in order to achieve more reliable design and planning of the network.

Publié le : 2014-01-01
EUDML-ID : urn:eudml:doc:271863
@article{bwmeta1.element.bwnjournal-article-amcv24i3p669bwm,
     author = {D. Thresh Kumar and Hamed Soleimani and Govindan Kannan},
     title = {Forecasting return products in an integrated forward/reverse supply chain utilizing an ANFIS},
     journal = {International Journal of Applied Mathematics and Computer Science},
     volume = {24},
     year = {2014},
     pages = {669-682},
     zbl = {1322.93059},
     language = {en},
     url = {http://dml.mathdoc.fr/item/bwmeta1.element.bwnjournal-article-amcv24i3p669bwm}
}
D. Thresh Kumar; Hamed Soleimani; Govindan Kannan. Forecasting return products in an integrated forward/reverse supply chain utilizing an ANFIS. International Journal of Applied Mathematics and Computer Science, Tome 24 (2014) pp. 669-682. http://gdmltest.u-ga.fr/item/bwmeta1.element.bwnjournal-article-amcv24i3p669bwm/

[000] Brdyś, M.A., Borowa, A., Idźkowiak, P. and Brdyś, M.T. (2009). Adaptive prediction of stock exchange indices by state space wavelet networks, International Journal of Applied Mathematics and Computer Science 19(2): 337-348, DOI: 10.2478/v10006-009-0029-z. | Zbl 1169.91429

[001] Chang, F.-J. and Chang, Y.-T. (2006). Adaptive neuro-fuzzy inference system for prediction of water level in reservoir, Advances in Water Resources 29(1): 1-10.

[002] Chen, S.-H., Lin, Y.-H., Chang, L.-C. and Chang, F.-J. (2006). The strategy of building a flood forecast model by neuro-fuzzy network, Hydrological Processes 20(7): 1525-1540.

[003] Chittamvanich, S. and Ryan, S.M. (2011). Using forecasted information from early returns of used products to set remanufacturing capacity, Iowa State University, Ames, IA.

[004] Diebold, F.X. and Mariano, R.S. (1995). Comparing predictive accuracy, Journal of Business & Economic Statistics 13(3): 253-263.

[005] Efendigil, T., Önüt, S. and Kahraman, C. (2009). A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models: A comparative analysis, Expert Systems with Applications 36(3): 6697-6707.

[006] Georgiadis, P. (2013). An integrated system dynamics model for strategic capacity planning in closed-loop recycling networks: A dynamic analysis for the paper industry, Simulation Modelling Practice and Theory 32(1): 116-137.

[007] Guide, V.D.R. and Van Wassenhove, L.N. (2001). Managing product returns for remanufacturing, Production and Operations Management 10(2): 142-155.

[008] Jang, J.-S. (1993). ANFIS: Adaptive-network-based fuzzy inference system, IEEE Transactions on Systems, Man and Cybernetics 23(3): 665-685.

[009] Jun Li, R. and Xiong, Z.-B. (2005). Forecasting stock market with fuzzy neural networks, Proceedings of the 2005 International Conference on Machine Learning and Cybernetics, 2005, Guangzhou, China, Vol. 6, pp. 3475-3479.

[010] Kannan, D., Jafarian, A., Khamene, H. and Olfat, L. (2013). Competitive performance improvement by operational budget allocation using ANFIS and fuzzy quality function deployment: A case study, International Journal of Advanced Manufacturing Technology 68(1-4): 849-862.

[011] Krarup, J. and Pruzan, P.M. (1983). The simple plant location problem: Survey and synthesis, European Journal of Operational Research 12(1): 36-81. | Zbl 0506.90018

[012] Liao, H.P., Su, J.P. and Wu, H. (2001). An application of ANFIS to modeling of a forecasting system for the demand of teacher human resources, Journal of Education and Psychology 24(1): 1-17.

[013] Marx-Gomez, J., Rautenstrauch, C., Nürnberger, A. and Kruse, R. (2002). Neuro-fuzzy approach to forecast returns of scrapped products to recycling and remanufacturing, Knowledge-Based Systems 15(12): 119-128.

[014] Ozkr, V. and Balgil, H. (2013). Multi-objective optimization of closed-loop supply chains in uncertain environment, Journal of Cleaner Production 41(0): 114-125.

[015] Schrijver, A. (2004). Combinatorial Optimization: Polyhedra and Efficiency (Algorithms and Combinatorics), Springer, Berlin. | Zbl 1072.90030

[016] Sfetsos, A. (2000). A comparison of various forecasting techniques applied to mean hourly wind speed time series, Renewable Energy 21(1): 23-35.

[017] Sivasankaran, S., Sivaprasad, K., Narayanasamy, R. and Iyer, V.K. (2011). Evaluation of compaction equations and prediction using adaptive neuro-fuzzy inference system on compressibility behavior of AA 6061 100-x-wt.% Ti O2 nanocomposites prepared by mechanical alloying, Powder Technology 209(1-3): 124-137.

[018] Soleimani, H., Seyyed-Esfahani, M. and Kannan, G. (2014). Incorporating risk measures in closed-loop supply chain network design, International Journal of Production Research 52(6): 1843-1867.

[019] Soleimani, H., Seyyed-Esfahani, M. and Shirazi, M. (2013). Designing and planning a multi-echelon multi-period multi-product closed-loop supply chain utilizing genetic algorithm, International Journal of Advanced Manufacturing Technology 68(1-4): 917-931.

[020] Srivastava, S.K. (2006). Managing product returns for reverse logistics, International Journal of Physical Distribution and Logistics Management 36(7): 524-546.

[021] Sumi, S.M., Zaman, M.F. and Hirose, H. (2012). A rainfall forecasting method using machine learning models and its application to the Fukuoka city case, International Journal of Applied Mathematics and Computer Science 22(4): 841-854, DOI: 10.2478/v10006-012-0062-1. | Zbl 1283.68305

[022] Temur, G. and Bolat, B. (2012). Reverse logistics network design integrated with product return forecasting approach, 17th International Working Seminar on Production Economics, Innsbruck, Austria, pp. 483-497.

[023] Toktay, B., van der Laan, E.A. and de Brito, M.P. (2003). Managing product returns: The role of forecasting, ERIM Report ERS-2003-023-LIS, Erasmus University Rotterdam, Rotterdam.

[024] Wei, L.-Y. (2011). A fusion ANFIS model for forecasting EPS of leading industries in Taiwan, 2011 International Conference on Machine Learning and Cybernetics (ICMLC), Singapore, Vol. 1, pp. 1-4.

[025] Xu, X. and Fan, T. (2009). Forecast for the amount of returned products based on wave function, 2009 International Conference on Information Management, Innovation Management and Industrial Engineering, Xi'an, China, Vol. 2, pp. 324-327.

[026] Yun, Z., Quan, Z., Caixin, S., Shaolan, L., Yuming, L. and Yang, S. (2008). RBF neural network and ANFIS-based short-term load forecasting approach in real-time price environment, IEEE Transactions on Power Systems 23(3): 853-858.