A Performance Model for Designing Network in Reverse Logistic
Commenced in January 2007
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Edition: International
Paper Count: 32797
A Performance Model for Designing Network in Reverse Logistic

Authors: S. Dhib, S. A. Addouche, T. Loukil, A. Elmhamedi

Abstract:

In this paper, a reverse supply chain network is investigated for a decision making. This decision is surrounded by complex flows of returned products, due to the increasing quantity, the type of returned products and the variety of recovery option products (reuse, recycling, and refurbishment). The most important problem in the reverse logistic network (RLN) is to orient returned products to the suitable type of recovery option. However, returned products orientations from collect sources to the recovery disposition have not well considered in performance model. In this study, we propose a performance model for designing a network configuration on reverse logistics. Conceptual and analytical models are developed with taking into account operational, economic and environmental factors on designing network.

Keywords: Reverse logistics, Network design, Performance model, Open loop configuration.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1111985

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References:


[1] J. U. Duncombe, “Infrared navigation—Part I: An assessment of (1) Shui-Mu Huang, Jack C.P.Su. Impact of product proliferation on the reverse supply chain. Omega 2013; 41:626-639.
[2] Qiang Q, Ke K, Anderson T. June Dong. The closed loop supply chain network with competition, distribution, channel investment, and uncertainties. Omega 2013; 41:186-194.
[3] Farahani Z R, Rezapour S, Drezner T, Fallah S. Competitive supply chain network design: An overview of classification, models solution techniques and applications. Omega 2013; http:// dx.doi.org/10.1016/j.omega.2013.08.006.
[4] Mutha A, Pokharel S. Strategic network design for reverse logistics and remanufacturing using new and old product modules. Computers and Industrial Engineering 2009; 56:334–346.
[5] Francas D, Minner S. Manufacturing network configuration in supply chains with product recovery. Omega 2009; 37:757–769.
[6] Wadhwaa S., Madaana J., Chan F.T.S. Flexible decision modeling of reverse logistics system: A value adding MCDM approach for alternative selection. Robotics and Computer-Integrated Manufacturing 2009; 25 :460–469.
[7] Dhouib D. An extension of MACBETH method for fuzzy environment to analyze alternatives in reverse logistics for automobile tire wastes, Omega 2014; 42: 25-32.
[8] Dhib S, Addouche SA, Loukil Taicir Elmhamdi A. Selecting best configuration Selecting Configuration of Reverse Logistics Network Using Sustainability Indicators, International conference of modeling simulation and applied optimization. 2012 IEEE.
[9] Srivastava S K. Network design for reverse logistics. Omega 2008; 36: 535-548.
[10] Stock J K. Reverse logistics. white paper. council of logistics management. IL: Oak Brook. 1992.
[11] Ramezani R, Bashiri M, Moghaddam RT. A new multi objective stochastic model for a forward/reverse logistic network design with responsivesness and quality level. Applied Mathematical modeling 2013; 37: 328-344.
[12] Jayarman V, Jr GVDR, Srivastava R, A closed-loop logistics model for re manufacturing, Journal Operational Research Society 1999; 50:497-508.
[13] Listes O, and Dekker R, 2005, A stockatic approach to a case study for product recovery network design. European Journal of Operational Research 2005; 160:268-287.
[14] Aras N,Aksen D, Tanugur AG Locating collection centers for incentive- dependent returns under a pickup policy with capacitated vehicles European Journal of Operational Research 2008;191:1223-1240.
[15] Neely A, Gregory M, Platts K. Performance measurement systems design: a literature review and research agenda. International Journal of Operations & Production Management 1995; 15: 80-116.
[16] Dominique E, Lamouri S, ParisJ-L, Brahim DS. A framework for analysing supply chain performance evaluation models. International Journal Production Economics 2010; doi: 10.1016/j.ijpe. 2010.11.024.
[17] SCOR 2010. /http://www.supply-chain.orgS.
[18] Lee DH, Dong M. Dynamic network design for reverse logistics operations under uncertainty. Transportation Research Part E 2009; 45: 61–71.
[19] Gungor A, Gupta S M. Issues in environmentally conscious manufacturing and product recovery: a survey Computers & Industrial Engineering 1999; 36: 811-853.
[20] Amin S H, Zhang G. A multi-objective facility location model for closed-loop supply chain network under uncertain demand and return. Applied Mathematical Modelling 2012; 39: 4165–4176.
[21] Guillen G, Mele FD, Bagajewicz MJ, Espuna A, Puigjaner L, Multi objective supply chain design under uncertainty, Chemical Engineering Science 2005; 60:1535–1553.
[22] Azaron A, Borwn KN, Tarim SA, Modarres M, A multi-objective stochastic programming approach for supply chain design considering risk, International Journal Production Economics 2008; 116: 129–138.
[23] Franca RB., Jones EC, Richards CN, Carlson JP. Multi-objective stochastic supply chain modeling to evaluate tradeoffs between profit and quality. International Journal of Production Economics 2009; 127: 292–299.
[24] EFQM, 2010. /http://www.efqm.org.