A DEA model with dual-role factors and fuzzy data for selecting third-party reverse logistics provider, case study: Hospital waste collection

Abstract

Today, most institutions and companies delegate reverse logistics services, organizational communication, and outsourcing company to a third-party logistics reverse provider. To select an appropriate third-party logistics provider, this study proposes a decision-making procedure considering dual role data and fuzzy states based on data envelopment analysis models. After determining evaluation indices, this study uses the α-cut approach to transform the proposed model under uncertainty to a deterministic model. Finally, a case study (collection and burial of hospital waste) is presented to show the application of this research. Results of selecting the best third-party logistics provider and more analyses are provided after coding in GAMS software.

Keywords

• Reverse logistics
• Outsourcing
• Third-party logistics provider
• Data envelopment analysis
• Dual role factors
• Inaccurate data