A sustainable cross-efficiency DEA model for international MSW-to-biofuel supply chain design

School of Industrial Engineering, Iran University of Science & Technology, Narmak, IR 16846-13114, Tehran

^* Corresponding author: hadi_sahebi@iustacir

Received: 14 May 2019
Accepted: 20 September 2020

Abstract

Fossil fuels, as the primary source of the energy supply in today’s global society, are being depleted much faster than expected and are raising serious environmental and social concerns for contemporary societies. To deal with issues, a global movement towards the generation of sustainable renewable energy is underway. One of the most promising sources of renewable energy alternatives is the use of municipal solid waste, as a biomass source since it does not endanger food security and considerably the biomass made by municipal solid waste will enable the appropriate management of the waste and help cities to be sustainable. The supply chain of converting the municipal solid waste to bioenergy is a challenging issue that have attracted the attention of academic and industrial research. In this direction, a three-echelon mathematical model is developed to design MSW-to-biofuel supply chain network. This supply network is a global network; hence, the international supply chain-related issues and the disruption in the raw material supply have also been studied. Identifying appropriate potential locations to site facilities is a challenge faced in the municipal solid waste-to-biofuel supply chain models. To achieve goal, in this research, the use has been made of a proposed sustainable cross-efficiency DEA model which is an effective ranking method, especially for finding potential points. To deal with sustainability, the social and environmental indicators have also been presented in the form of some criteria in this DEA method. In addition, effort has been made to improve the ecological indicators of the supply chain design in line with the sustainable development as an objective function. Finally, in order to validate the proposed model, a case study with real data is presented.