Authors

1 Department of Industrial Engineering, Najafabad Branch, Islamic Azad University, Isfahan, Iran

2 School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.

3 Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran

Abstract

The attestation towards environmental, statutory obligations and economic interests arising from rehabilitation operations in recent years has led to more focus on reverse logistics operations. To this end, integration of the design of reverse and forward logistics networks which results in prevention of sub optimality due to separated design of these networks is of high significance. This model discusses a mixed-integer nonlinear programming model for the integrated design of multi-level and multi-commodity forward-reverse supply chain network. In the end, the calculation results of the proposed model solution have been presented via GAMS software in order to locate facilities, determine the relationship between facilities and raw material procurement rate and production rate. The result of the solved multi-purpose models has corroborated the single-purpose models and it shows the efficiency of the used methods.

Keywords

Amin, S.H. and Zhang, G., (2012). “An integrated model for closed-loop supply chain configuration and supplier selection: Multi-objective approach”, Expert Systems with Applications, Vol. 39, No. 8, pp. 6782-6791.
Amin, S.H. and Zhang, G., (2013). “A multi-objective facility location model for closed-loop supply network under uncertain demand and return”, Applied Mathematical Modelling, Vol.37, No. 6, pp. 4165-4176.
Amiri, A. (2006), “Designing a distribution network in a supply chain system: formulation and efficient solution procedure”, European Journal of Operational Research, Vol.171, No. 2, pp. 567–576.
Arabzad, S.M. Ghorbani, M. and Tavakkoli-moghaddam, R. (2014) “An evolutionary algorithm for a new multi-objective location-inventory model in a distribution network with transportation modes and third-party logistics providers”, International Journal of Production Research, (In Press), (1-13).
Bernon, M. and Cullen, J. (2007). “An integrated approach to managing reverse logistics.” International Journal of Logistics: Research and Applications, Vol. 10, No. 1, pp. 41-56.
Demirel, N., Ozceylan, E., Paksoy, T. and Gokcen, H., (2014). “A genetic algorithm approach for optimizing a closed-loop supply chain network with crisp and fuzzy objectives”, International Journal of Production Research, Vol.52, No. 12, pp. 3637-3664.
Eskandarpour M, Zegordi S.H. and Nikbakhsh E. (2012), “A parallel variable neighbourhood search for the multi-objective sustainable post-sales network design problem”, International Journal of Production Economics, Vol. 145, No. 1, pp. 117–131.
Fallah-Tafti, A., Sahraeian, R., Tavakkoli-Moghaddam, R. and Moeinipour, M., (2014). “An interactive possibility programming approach for a multi-objective closed-loop supply chain network under uncertainty”, International Journal of Systems Science, Vol.45, No. 3, pp. 283-299.
Fleischmann, M., Beullens, P., Bloemhof-Ruwaard, JM., and Wassenhove, L. (2001). “The impact of product recovery on logistics network design.” Production and Operations Management, Vol. 10, No. 2, pp. 156-173.
Ghorbani, M., Arabzad, S.M. and Tavakkoli-Moghaddam, R. (2014). “A Multi-objective Fuzzy Goal Programming Model for Reverse Supply Chain Design”, International Journal of Operational Research, Vol. 19, No.2, pp. 141-153.
Hatefi, S.M., F. Jolai, (2013). “Robust and reliable forward–reverse logistics network design under demand uncertainty and facility disruptions,” Applied Mathematical Modelling, Vol. 38, No. 9–10, PP. 2630-2647.
Hatefi, S.M. and Jolai, F. (2014). “Robust and reliable forward–reverse logistics network design under demand uncertainty and facility disruptions”, Applied Mathematical Modelling, Vol. 38, No. 9, pp. 9-10.
Keyvanshokooh, E., Fattahi, M., Seyed-Hosseini, S.M., and Tavakkoli-Moghaddam, R., (2013).“A dynamic pricing approach for returned products in integrated forward/reverse logistics network design”, Applied Mathematical Modelling, Vol. 37, No. 24, pp. 10182- 10202.
Lee, D.H. and Dong, M. (2008). “A heuristic approach to logistics network design for end-oflease computer products recovery”, Transportation Research E, Vol. 44, No. 3, pp. 455- 474.
Mavrotas, G. and Florios, K., (2013), “An improved version of the augmented ε-constraint method (AUGMECON2) for finding the exact pare to set in multi-objective integer programming problems”, Applied Mathematics and Computation, Vol. 219, No.18, pp. 9652-9669.
Özceylan, E., Paksoy, T. and Bektaş, T. (2014). “Modelling and optimizing the integrated problem of closed-loop supply chain network design and disassembly line balancing”, Transportation Research Part E: Logistics and Transportation Review, Vol. 61, No. 1, pp.142-164.
Pishvaee, M. S., Rabbani, M., and Torabi, S. A. (2011). “A robust optimization approach to closed-loop supply chain network design under uncertainty”, Applied Mathematical Modelling, Vol. 35, No. 4, pp. 637–649.
Pishvaee, M.S., Jolai, F. and Razmi, J. (2009). “A stochastic optimization model for integrated forward/reverse logistics network design.” Journal of Manufacturing Systems, Vol. 28, No. 1, pp. 107-114.
Pishvaee, M.S., Razmi, J. and Torabi, S.A., (2014). “An accelerated Benders decomposition algorithm for sustainable supply chain network design under uncertainty: A case study of medical needle and syringe supply chain”, Transportation Research Part E: Logistics and Transportation Review, Vol. 67, No. 1, pp. 14-38.
Ramezani, M., Bashiri, M. and Tavakkoli-Moghaddam, R., (2013). “A new multi-objective stochastic model for a forward/reverse logistic network design with responsiveness and quality level”, Applied MathematicalModel, Vol. 37, No. 1-2, pp. 328-344.
Ramezani, M., Kimiagari, A.M., Karimi, B. and Hejazi, T.H., (2014). “Closed-loop supply chain network design under a fuzzy environment”, Knowledge-Based Systems, Vol. 59, No. 1, pp. 108-120.
Santoso T, Ahmed S, Goetschalckx M. and Shapiro A. (2005). “A stochastic programming approach for supply chain network design under uncertainty”, European Journal of Operational Research, Vol. 167, No. 1, pp. 96-115.
Tabrizi, B.H. and Razmi, J., (2013). “Introducing a mixed-integer non-linear fuzzy model for risk management in designing supply chain networks”, Journal of Manufacturing Systems, Vol. 32, No. 2, pp. 295-307.
Vahdani, B., Razmi, J. and Tavakkoli-Moghaddam, R. (2012), “Fuzzy possibility modelling for closed-loop recycling collection networks”, Environmental Modelling and Assessment, Vol. 17, No. 6, pp. 623–637.
Vahdani, B., Tavakkoli-Moghaddam, R., Jolai, F. and Baboli, A., (2014). “Reliable design of a closed loop supply chain network under uncertainty”, an interval fuzzy possibility chance-constrained model, Vol. 45, No. 6, pp. 745-765.