A Decision Making Methodology for the Selection of Reverse Logistics Operating Channels

Abstract An efficient management of product returns is a strategic issue. Nowadays, customer expect manufacturer to develop a reverse logistics system so that the returned products can be recovered. With the development and advancement of reverse logistics practice, the selection of reverse logistics operating channels becomes more important. There are three operating channels of reverse logistics; Manufacturer Operation, Third Party Operation, Joint Operation. In this paper a hybrid methodology based on Analytical Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) under fuzzy environment is proposed for the selection and evaluation of reverse logistics operating channels. An example is included to validate the proposed method. This method helps the decision maker to select the best technology that meets the requirement.

[1]  Manoj Kumar Tiwari,et al.  Analyzing alternatives in reverse logistics for end-of-life computers: ANP and balanced scorecard approach , 2005, Comput. Ind. Eng..

[2]  S. Dowlatshahi,et al.  A strategic framework for the design and implementation of remanufacturing operations in reverse logistics , 2005 .

[3]  Hokey Min,et al.  THE DYNAMIC DESIGN OF A REVERSE LOGISTICS NETWORK FROM THE PERSPECTIVE OF THIRD-PARTY LOGISTICS SERVICE PROVIDERS , 2008 .

[4]  Patricia J. Daugherty,et al.  The impact of operating environment on the formation of cooperative logistics relationships , 1997 .

[5]  Matthias Zimmermann,et al.  Performance evaluation as an influence factor for the determination of profit shares of competence cells in non-hierarchical regional production networks , 2006 .

[6]  Dan Andersson,et al.  Procurement of logistics services—a minutes work or a multi-year project? , 2002 .

[7]  Tom Andel,et al.  ADVANCE WITH REVERSE LOGISTICS. , 1995 .

[8]  Luk N. Van Wassenhove,et al.  Closed - Loop Supply Chain Models with Product Remanufacturing , 2004, Manag. Sci..

[9]  C. Hwang Multiple Objective Decision Making - Methods and Applications: A State-of-the-Art Survey , 1979 .

[10]  Ronald S. Tibben-Lembke,et al.  AN EXAMINATION OF REVERSE LOGISTICS PRACTICES , 2001 .

[11]  Robert E. Spekman,et al.  Characteristics of partnership success: Partnership attributes, communication behavior, and conflict resolution techniques , 1994 .

[12]  Ming-Lang Tseng,et al.  Green supply chain management with linguistic preferences and incomplete information , 2011, Appl. Soft Comput..

[13]  Irfan Ertugrul,et al.  Performance evaluation of Turkish cement firms with fuzzy analytic hierarchy process and TOPSIS methods , 2009, Expert Syst. Appl..

[14]  Mitsuru Ishizuka,et al.  Emerging topic tracking system in WWW , 2006, Knowl. Based Syst..

[15]  Festus Olorunniwo,et al.  An exploration of reverse logistics practices in three companies , 2008 .

[16]  Semih Onüt,et al.  Transshipment site selection using the AHP and TOPSIS approaches under fuzzy environment. , 2008, Waste management.

[17]  M. Razzaque,et al.  Outsourcing of logistics functions: a literature survey , 1998 .

[18]  Shaligram Pokharel,et al.  Strategic network design for reverse logistics and remanufacturing using new and old product modules , 2009, Comput. Ind. Eng..

[19]  Laura Meade,et al.  A conceptual model for selecting and evaluating third‐party reverse logistics providers , 2002 .

[20]  G. Kannan,et al.  Analysis of closed loop supply chain using genetic algorithm and particle swarm optimisation , 2009 .

[21]  Paul R. Kleindorfer,et al.  Integrating manufacturing strategy and technology choice , 1990 .

[22]  Arun Kumar,et al.  A decision‐making model for reverse logistics in the computer industry , 2006 .

[23]  Samir K. Srivastava,et al.  Managing product returns for reverse logistics , 2006 .

[24]  Mike Bernon,et al.  Retail reverse logistics: a call and grounding framework for research , 2011 .

[25]  R. M. Monczka,et al.  Supply Base Strategies to Maximize Supplier Performance , 1993 .

[26]  C. Kahraman,et al.  Fuzzy multi-attribute equipment selection based on information axiom , 2005 .

[27]  Beth M Schwartz REVERSE LOGISTICS STRENGTHENS SUPPLY CHAINS , 2000 .

[28]  Ashutosh Tiwari,et al.  A review of soft computing applications in supply chain management , 2010, Appl. Soft Comput..

[29]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..