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2014 | 24 | 3 | 669-682
Tytuł artykułu

Forecasting return products in an integrated forward/reverse supply chain utilizing an ANFIS

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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.
Rocznik
Tom
24
Numer
3
Strony
669-682
Opis fizyczny
Daty
wydano
2014
otrzymano
2013-08-09
poprawiono
2013-11-04
poprawiono
2014-02-21
Twórcy
  • Department of Mechanical Engineering, PTR College of Engineering and Technology, Thangapandiyan Nagar, Austinpatti (Po) Madurai, 625008, Tamil Nadu, India
  • Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University (IAU), Barajin, Nokhbegan Boulevard, Qazvin, Iran
  • Department of Business and Economics, University of Southern Denmark, Campusvej 55, Odense M, 5230, Denmark
Bibliografia
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  • 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.
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  • Jang, J.-S. (1993). ANFIS: Adaptive-network-based fuzzy inference system, IEEE Transactions on Systems, Man and Cybernetics 23(3): 665-685.
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  • 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.
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  • 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.
  • 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.
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  • Sfetsos, A. (2000). A comparison of various forecasting techniques applied to mean hourly wind speed time series, Renewable Energy 21(1): 23-35.
  • 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.
  • 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.
  • 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.
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Bibliografia
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