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A multicriteria decision making model for reverse logistics using analytical hierarchy process

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  • Barker, Theresa J.
  • Zabinsky, Zelda B.
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    Abstract

    Product recovery activities such as recycling, refurbishing and direct reuse are becoming integral to manufacturing supply chains. This study presents a multicriteria decision making model for reverse logistics using analytical hierarchy process (AHP). The AHP model evaluates a hierarchy of criteria and subcriteria, including costs and business relations, for critical decisions regarding network design. Using sensitivity analysis with AHP, the work provides insights into the preference ordering among eight alternative network configurations. For instance, the choice of test sites is largely dependent on the potential for cost savings on testing procedures and transportation of scrap, and this decision is not sensitive to the importance of business relations. By contrast, the choice of collection sites is largely determined by the relative importance of business relations considerations vs. cost considerations. As well, the processing location decision favors a third-party reprocessor if there is little need to protect proprietary product knowledge and cost savings is very important. The model is demonstrated using three case studies of real-world applications.

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    Bibliographic Info

    Article provided by Elsevier in its journal Omega.

    Volume (Year): 39 (2011)
    Issue (Month): 5 (October)
    Pages: 558-573

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    Handle: RePEc:eee:jomega:v:39:y:2011:i:5:p:558-573

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    Keywords: Reverse logistics Decision making models Product recovery Sensitivity analysis AHP;

    References

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    1. Fleischmann, Mortiz & Krikke, Hans Ronald & Dekker, Rommert & Flapper, Simme Douwe P., 2000. "A characterisation of logistics networks for product recovery," Omega, Elsevier, vol. 28(6), pages 653-666, December.
    2. Gerald W. Evans, 1984. "An Overview of Techniques for Solving Multiobjective Mathematical Programs," Management Science, INFORMS, vol. 30(11), pages 1268-1282, November.
    3. Guitouni, Adel & Martel, Jean-Marc, 1998. "Tentative guidelines to help choosing an appropriate MCDA method," European Journal of Operational Research, Elsevier, vol. 109(2), pages 501-521, September.
    4. Fleischmann, Moritz & Bloemhof-Ruwaard, Jacqueline M. & Dekker, Rommert & van der Laan, Erwin & van Nunen, Jo A. E. E. & Van Wassenhove, Luk N., 1997. "Quantitative models for reverse logistics: A review," European Journal of Operational Research, Elsevier, vol. 103(1), pages 1-17, November.
    5. Bautista, Joaquín & Pereira, Jordi, 2006. "Modeling the problem of locating collection areas for urban waste management. An application to the metropolitan area of Barcelona," Omega, Elsevier, vol. 34(6), pages 617-629, December.
    6. Listes, Ovidiu & Dekker, Rommert, 2005. "A stochastic approach to a case study for product recovery network design," European Journal of Operational Research, Elsevier, vol. 160(1), pages 268-287, January.
    7. Francas, David & Minner, Stefan, 2009. "Manufacturing network configuration in supply chains with product recovery," Omega, Elsevier, vol. 37(4), pages 757-769, August.
    8. Salema, Maria Isabel Gomes & Barbosa-Povoa, Ana Paula & Novais, Augusto Q., 2007. "An optimization model for the design of a capacitated multi-product reverse logistics network with uncertainty," European Journal of Operational Research, Elsevier, vol. 179(3), pages 1063-1077, June.
    9. Barros, A. I. & Dekker, R. & Scholten, V., 1998. "A two-level network for recycling sand: A case study," European Journal of Operational Research, Elsevier, vol. 110(2), pages 199-214, October.
    10. Min, Hokey & Jeung Ko, Hyun & Seong Ko, Chang, 2006. "A genetic algorithm approach to developing the multi-echelon reverse logistics network for product returns," Omega, Elsevier, vol. 34(1), pages 56-69, January.
    11. Spengler, Th. & Puchert, H. & Penkuhn, T. & Rentz, O., 1997. "Environmental integrated production and recycling management," European Journal of Operational Research, Elsevier, vol. 97(2), pages 308-326, March.
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    Cited by:
    1. Xu, Yan & Yeh, Chung-Hsing, 2012. "An integrated approach to evaluation and planning of best practices," Omega, Elsevier, vol. 40(1), pages 65-78, January.
    2. Qiang, Qiang & Ke, Ke & Anderson, Trisha & Dong, June, 2013. "The closed-loop supply chain network with competition, distribution channel investment, and uncertainties," Omega, Elsevier, vol. 41(2), pages 186-194.
    3. Ramanathan, Usha, 2013. "Aligning supply chain collaboration using Analytic Hierarchy Process," Omega, Elsevier, vol. 41(2), pages 431-440.
    4. Verdecho, María-José & Alfaro-Saiz, Juan-Jose & Rodriguez-Rodriguez, Raul & Ortiz-Bas, Angel, 2012. "A multi-criteria approach for managing inter-enterprise collaborative relationships," Omega, Elsevier, vol. 40(3), pages 249-263.
    5. Schulz, Tobias & Voigt, Guido, 2014. "A flexibly structured lot sizing heuristic for a static remanufacturing system," Omega, Elsevier, vol. 44(C), pages 21-31.
    6. Ruiz-Benítez, Rocío & Ketzenberg, Michael & van der Laan, Erwin A., 2014. "Managing consumer returns in high clockspeed industries," Omega, Elsevier, vol. 43(C), pages 54-63.
    7. Pang, Jifang & Liang, Jiye, 2012. "Evaluation of the results of multi-attribute group decision-making with linguistic information," Omega, Elsevier, vol. 40(3), pages 294-301.
    8. Mohamed Akli Achabou & Sihem Dekhili & Linda Prince, 2014. "Proactive CSR: Analytical Hierarchy Process And Empirical Assessment In The French Context," Working Papers 2014-075, Department of Research, Ipag Business School.

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