Developing an Output‐Oriented Super Slacks‐Based Measure Model with an Application to Third‐Party Reverse Logistics Providers

Outsourcing is an increasingly significant topic pursued via corporations seeking enhanced efficiency. Third-party reverse logistics involves the employ of external firms to carry out some or all of the firm's logistics activities. Output-oriented super slacks-based measure (SBM) model is one of the models in data envelopment analysis (DEA). In many real-world applications, data are often stochastic. A successful approach to address uncertainty in data is to replace deterministic data via random variables, leading to chance-constrained DEA. In this paper, a chance-constrained output-oriented super SBM model is developed and also its deterministic equivalent, which is a nonlinear program, is derived. Furthermore, it is shown that the deterministic equivalent of the stochastic output-oriented super SBM model can be converted into a quadratic program. In addition, sensitivity analysis of the stochastic output-oriented super SBM model is discussed with respect to changes on parameters. Finally, a numerical example demonstrates the application of the proposed model. Copyright © 2011 John Wiley & Sons, Ltd.

[1]  Steven A. Melnyk,et al.  Applying environmental criteria to supplier assessment: A study in the application of the Analytical Hierarchy Process , 2002, Eur. J. Oper. Res..

[2]  B. Çatay,et al.  Third‐party logistics provider selection: insights from a Turkish automotive company , 2007 .

[3]  Dusan Stefanovic,et al.  Methodology for modeling and analysis of supply networks , 2008, J. Intell. Manuf..

[4]  Ravi Shankar,et al.  Selection of logistics service provider: An analytic network process (ANP) approach , 2007 .

[5]  William W. Cooper,et al.  Stochastics and Statistics , 2001 .

[6]  Reza Farzipoor Saen,et al.  A mathematical model for selecting third-party reverse logistics providers , 2009 .

[7]  Timon C. Du,et al.  An evaluation of freight consolidation policies in global third party logistics , 2003 .

[8]  Manish Bachlaus,et al.  Designing an integrated multi-echelon agile supply chain network: a hybrid taguchi-particle swarm optimization approach , 2008, J. Intell. Manuf..

[9]  Francisco J. André,et al.  Indirect elicitation of non-linear multi-attribute utility functions. A dual procedure combined with DEA , 2009 .

[10]  Pekka Korhonen,et al.  Structural Comparison of Data Envelopment Analysis and Multiple Objective Linear Programming , 1998 .

[11]  A. Charnes,et al.  Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis , 1984 .

[12]  Lawrence M. Seiford,et al.  CHARACTERISTICS ON STOCHASTIC DEA EFFICIENCY : RELIABILITY AND PROBABILITY BEING EFFICIENT , 1999 .

[13]  Jie Cao,et al.  Integration of the Social Welfare Function and TOPSIS Algorithm for 3PL Selection , 2007, Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007).

[14]  N. Petersen,et al.  Chance constrained efficiency evaluation , 1995 .

[15]  Manoj Kumar,et al.  A fuzzy goal programming approach for vendor selection problem in a supply chain , 2004, Comput. Ind. Eng..

[16]  Gülçin Büyüközkan,et al.  Application of a hybrid intelligent decision support model in logistics outsourcing , 2007, Comput. Oper. Res..

[17]  René V. Mayorga,et al.  Supply chain management: a modular Fuzzy Inference System approach in supplier selection for new product development , 2008, J. Intell. Manuf..

[18]  Reza Farzipoor Saen,et al.  Performance measurement of power plants in the existence of weight restrictions via slacks‐based model , 2010 .

[19]  Wei-Kai Wang,et al.  An integrated fuzzy approach for provider evaluation and selection in third-party logistics , 2009, Expert Syst. Appl..

[20]  A. Rizzi,et al.  A fuzzy TOPSIS methodology to support outsourcing of logistics services , 2006 .

[21]  Fong-Yuen Ding,et al.  Using data envelopment analysis to compare suppliers for supplier selection and performance improvement , 2000 .

[22]  Joe Zhu,et al.  Chapter 15 DEA/AR analysis of the 1988–1989 performance of the Nanjing textiles corporation , 1996, Ann. Oper. Res..

[23]  Wenhuang Liu,et al.  An AHP/DEA Methodology for 3PL Vendor Selection in 4PL , 2005, CSCWD.

[24]  Valerie Belton,et al.  Demystifying DEA — A Visual Interactive Approach Based on Multiple Criteria Analysis , 1993 .

[25]  John N. Pearson,et al.  The impact of purchasing and supplier involvement on strategic purchasing and its impact on firm’s performance , 2002 .

[26]  A. Bounfour.  Is outsourcing of intangibles a real source of competitive advantage , 1999 .

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

[28]  Hokey Min,et al.  Benchmarking the operational efficiency of third party logistics providers using data envelopment analysis , 2006 .

[29]  Wei Guo,et al.  A Method of Third-Party Logistics Providers Selection and Transportation Assignments with FAHP and GP , 2008, 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing.

[30]  William W. Cooper,et al.  Chance constrained programming approaches to technical efficiencies and inefficiencies in stochastic data envelopment analysis , 2002, J. Oper. Res. Soc..

[31]  Irem Ozkarahan,et al.  An integrated multicriteria decision-making methodology for outsourcing management , 2007, Comput. Oper. Res..

[32]  Chang Seong Ko,et al.  A hybrid optimization/simulation approach for a distribution network design of 3PLS , 2006, Comput. Ind. Eng..

[33]  W. H. Ip,et al.  Combining scoring method and fuzzy expert systems approach to supplier assessment: a case study , 2002 .

[34]  Tugba Efendigil,et al.  A holistic approach for selecting a third-party reverse logistics provider in the presence of vagueness , 2008, Comput. Ind. Eng..

[35]  Rodney H. Green,et al.  Data envelopment analysis and multiple criteria decision making , 1993 .

[36]  Theodor J. Stewart.  Data envelopment analysis and multiple criteria decision making: A response , 1994 .

[37]  Theodor J. Stewart,et al.  Relationships between Data Envelopment Analysis and Multicriteria Decision Analysis , 1996 .

[38]  Majid Azadi,et al.  A chance‐constrained data envelopment analysis approach for strategy selection , 2011 .

[39]  Reza Farzipoor Saen,et al.  A new model for selecting third-party reverse logistics providers in the presence of multiple dual-role factors , 2010 .

[40]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .

[41]  Michel Simioni,et al.  Stochastic production frontiers and panel data: A latent variable framework , 1995 .

[42]  Sohail S. Chaudhry,et al.  A model of a decision support system based on case‐based reasoning for third‐party logistics evaluation , 2003, Expert Syst. J. Knowl. Eng..

[43]  Ines Herrero,et al.  A modified DEA model to estimate the importance of objectives with an application to agricultural economics , 2010 .

[44]  Denis Bouyssou,et al.  Using DEA as a tool for MCDM: some remarks , 1999, J. Oper. Res. Soc..

[45]  Reza Farzipoor Saen,et al.  Developing a WPF-CCR model for selecting suppliers in the presence of stochastic data , 2011, OR Insight.

[46]  Reza Farzipoor Saen,et al.  A new chance-constrained data envelopment analysis for selecting third-party reverse logistics providers in the existence of dual-role factors , 2011, Expert Syst. Appl..

[47]  Frederic H. Murphy,et al.  MANAGING REVERSE LOGISTICS CHANNELS WITH DATA ENVELOPMENT ANALYSIS , 2003 .