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Design of demand driven return supply chain for high-tech products

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DOI: 
http://dx.doi.org/10.3926/jiem.2011.v4n3.p481-503
Abstract (2. Language): 
Purpose: The purpose of this study is to design a responsive network for after-sale services of high-tech products. Design/methodology/approach: Analytic Hierarchy Process (AHP) and weighted max-min approach are integrated to solve a fuzzy goal programming model. Findings: Uncertainty is an important characteristic of reverse logistics networks, and the level of uncertainty increases with the decrease of the products’ life-cycle. Research limitations/implications: Some of the objective functions of our model are simplified to deal with non-linearities. Practical implications: Designing after-sale services networks for high-tech products is an overwhelming task, especially when the external environment is characterized by high levels of uncertainty and dynamism. This study presents a comprehensive modeling approach to simplify this task. Originality/value: Consideration of multiple objectives is rare in reverse logistics network design literature. Although the number of multi-objective reverse logistics network design studies has been increasing in recent years, the last two objective of our model is unique to this research area.

REFERENCES

References: 

Altıparmak, F., Gen, M., Lin, L., & Paksoy, T. (2006). A genetic algorithm approach for multi-objective optimization of supply chain networks. Computers & Industrial Engineering, 51, 196-215. http://dx.doi.org/10.1016/j.cie.2006.07.011
Amaro, A.C.S., & Barbaso-Povoa, A.P.F.D. (2009). The effect of uncertainty on the optimal closed-loop supply chain planning under different partnerships structure. Computers and Chemical Engineering, 33, 2144-2158. http://dx.doi.org/10.1016/j.compchemeng.2009.06.003
Amid, A., Ghodsypour S.H., & O’Brien, C. (2011). A weighted max-min model for fuzzy multi-objective supplier selection in a supply chain. International Journal of Production Economics, 131(1), 139-145. http://dx.doi.org/10.1016/j.ijpe.2010.04.044
Beamon, B.M., & Fernandes C. (2004). Supply-chain network configuration for product recovery. Production Planning & Control, 15(3), 270–281. http://dx.doi.org/10.1080/09537280410001697701
Biehl, M., Preter, E., Realff, & M.J. (2007). Assesing performance and uncertainity in developing carpet reverse logistics systems. Computers & Operations Research, 34, 443-463. http://dx.doi.org/10.1016/j.cor.2005.03.008
Bundschuh R.G., & Dezvane T.M. (2003). How to make after sale services pay off. The McKinsey Quarterly, 4, 116–127.
Chan, F.T.S., Chung, S.H., & Wadhwa, S. (2005). A hybrid genetic algorithm for production and distribution. Omega, 33, 345-355. http://dx.doi.org/10.1016/j.omega.2004. 05.004
Cheng, Y.H., & Lee, F. (2010). Outsourcing reverse logistics of high-tech manufacturing firms by using a systematic decision-making approach: TFT-LCD sector in Taiwan, Industrial Marketing Management, 39(7), 1111-1119. http://dx.doi.org/10.1016/j.indmarman.2009.10.004
Chouinard, M., D’Amours, S., & Ait-Kadi, D. (2008). A stochastic programming approach for designing supply loops. International Journal of Production Economics, 113, 657-677. http://dx.doi.org/10.1016/j.ijpe.2007.10.023
D’Cruz, C.A. (2010). Strategic analysis tools for high tech marketing, Xodus Business Technological Solutions. www.xodusBTS.com - Accessed 28th July 2010.
Journal of Industrial Engineering and Management - http://dx.doi.org/10.3926/jiem.2011.v4n3.p481-503
- 501 -
Du, F., & Ewans, G.W. (2008). A bi-objective reverse logistics network analysis for post-sale service. Computers & Operations Research, 35, 2617-2634.
El-Sayed, M., Afia, N., & El-Kharbotly, A. (2010). A stochastic model for forward-reverse logistics network design under risk. Computers & Industrial Engineering, 58(3), 423-431. http://dx.doi.org/10.1016/j.cie.2008.09.040
Hong, I.H., Assavapokee, T., Ammons, J., Boelkins, C., Gilliam, K., Oudit, D., Realff, M., Vannicola, J.M., & Wongthatsanekorn, W. (2006). Planning the e-scrap reverse production system under uncertainty in the state of Georgia: A case study. IEEE Transactions on Electronics Packaging Manufacturing, 29, 150-162. http://dx.doi.org/10.1109/TEPM.2006.881769
Ilgın, M.A., & Gupta, S.M. (2010). Environmentally conscious manufacturing and product recovery (ECMPRO): A review of the state of the art. Journal of Environmental Management, 91, 563-591. http://dx.doi.org/10.1016/j.jenvman.2009.09.037
Kongar, E., & Gupta, S. (2006). Disassembly to order system under uncertainty. Omega, 34, 550-561. http://dx.doi.org/10.1016/j.omega.2005.01.006
Lee, D.H., & Dong, M. (2009). Dynamic network design for reverse logistics operations under uncertainty. Transportation Research Part E, 45, 61-71. http://dx.doi.org/10.1016/j.tre.2008.08.002
Liang, T.F. (2006). Distribution planning decisions using interactive fuzzy multi-objective linear programming. Fuzzy Sets and Systems, 157, 1303-1316. http://dx.doi.org/10.1016/j.fss.2006.01.014
Liang, T.F. (2009). Fuzzy multi-objective project management decisions using two-phase fuzzy goal programming approach. Computers & Industrial Engineering, 57, 1407-1416. http://dx.doi.org/10.1016/j.cie.2009.07.010
Lieckens, K., & Vandaele, N. (2007). Reverse logistics network design with stochastic lead time. Computers & Operations Research, 34, 395-416. http://dx.doi.org/10.1016/j.cor.2005.03.006
Lin, C. (2004). A weighted max-min model for fuzzy goal programming. Fuzzy Sets and Systems, 142, 407-420. http://dx.doi.org/10.1016/S0165-0114(03)00092-7
Listeş, O. (2007). A generic stochastic model for supply-and-return network design. Computers & Operations Research, 34, 417-442. http://dx.doi.org/10.1016/j.cor.2005.03.007
Journal of Industrial Engineering and Management - http://dx.doi.org/10.3926/jiem.2011.v4n3.p481-503
- 502 -
Listeş, O., & Dekker, R. (2005). A stochastic approach to a case study for product recovery network design. European Journal of Operational Research, 160, 268-287. http://dx.doi.org/10.1016/j.ejor.2001.12.001
Pishvaee, M.S., Farahani, R.Z., & Dullaert, W. (2010). A memetic algorithm for bi-objective integrated forward/reverse logistics network design. Computers & Industrial Engineering, 37, 1100-1112.
Qin, Z., & Ji, X. (2010). Logistics network design for product recovery in fuzzy environment. European Journal of Operational Research, 202, 479-490. http://dx.doi.org/10.1016/j.ejor.2009.05.036
Realff, M.J., Ammons, J.C., & Newton, D. (2000). Strategic design of reverse production systems. Computers and Chemical Engineering, 24, 991-996. http://dx.doi.org/10.1016/S0098-1354(00)00418-X
Realff, M.J., Ammons, J.C., & Newton, D. (2004). Robust reverse production system design for carpet recycling. IIE Transactions, 36, 767-776. http://dx.doi.org/10.1080/07408170490458580
Saaty, T.L. (1980). Analytical Hierarchy Process: Planning, Priority setting, resource allocation. New York and London: McGraw-Hill International Book Co.
Salema, M.I.G., Barbosa-Povoa, A.P., & Novais, A.Q. (2007). An optimization model for the design of a capacitated multi-product reverse logistics network with uncertainty. European Journal of Operational Research, 179, 1063-1077. http://dx.doi.org/10.1016/j.ejor.2005.05.032
Srivastava, S.N. (2008). Network design for reverse logistics. Omega, 36(4), 535-548. http://dx.doi.org/10.1016/j.omega.2006.11.012
Thrikutam, P., & Kumar, S. (2004). Turning Returns Management into a Competitive Advantage in Hi-Tech Manufacturing, Infoys Technologies Ltd. http://www.mid-hudsonapics.org/LinkedDocuments/Infosys_Returns_Managemen... - Accessed 28th July 2010.
Tuzkaya, G., & Gülsün, B. (2008). Evaluating centralized return centers in a reverse logistics network: an integrated fuzzy multi-criteria decision approach. International Journal of Environmental Science and Technology, 5(3), 339-352.
Tuzkaya, G., Gülsün, B., & Onsel, Ş. (2011). A methodology for the strategic design of reverse logistics networks and its application in the Turkish white goods
Journal of Industrial Engineering and Management - http://dx.doi.org/10.3926/jiem.2011.v4n3.p481-503
- 503 -
industry. International Journal of Production Research, 49(15), 4543-4571. http://dx.doi.org/10.1080/00207543.2010.492804
White, C.D., Masanet, E., Rosen, C.M., & Beckman, S.L. (2003). Product recovery with some byte: An overview of management challenges and environmental consequences in reverse manufacturing for the computer industry. Journal of Cleaner Production, 11, 445-458. http://dx.doi.org/10.1016/S0959-6526(02)00066-5
Xiao, T., Shi, K., & Yang, D. (2010). Coordination of a supply chain with consumer return under demand uncertainty. International Journal of Production Economics, 124, 171-180. http://dx.doi.org/10.1016/j.ijpe.2009.10.021
Yongsheng, Z., & Shouyang, W. (2008). Generic model of reverse logistics network design. Journal of Transportation Systems Engineering and Information Technology, 8(3), 71-78. http://dx.doi.org/10.1016/S1570-6672(08)60025-2
Zanjirani Farahani, R., SteadieSeifi, M., & Asgari, N. (2010). Multiple criteria facility location problems: A survey. Applied Mathematical Modelling, 34, 1689–1709. http://dx.doi.org/10.1016/j.apm.2009.10.005
Zhang, L., Wang, Z., Pan, X., & Dong, T. (2010). Optimization Model for Remanufacturing Logistics Network with Fuzzy Parameters. International Conference on Measuring Technology and Mechatronics Automation. http://doi.ieeecomputersociety.org/10.1109/ICMTMA.2010.696 - Accessed 28th July 2010.

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