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

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.

Integrating Resilience and Sustainability Criteria in the Supply Chain Network Design. A Systematic Literature Review

“…Modeling Approach [60] Robust Programming [90] Robust Mixed Integer Linear Programming (ROMILP) [55] Mixed Integer Linear Programming (MILP) [91] Mixed Integer Linear Programming (MILP) [92] Mixed Integer Linear Programming (MILP) [93] Mixed Integer Non-Linear Programming (MINLP) [67] Mixed Integer Linear Programming (MILP) [94] Mixed Integer Linear Programming (MILP) [95] Mixed Integer Non-Linear Programming (MINLP) [68] Mixed Integer Linear Programming (MILP) [96] Stochastic Mixed-Integer Programming [97] Robust Programming [98] Mixed Integer Linear Programming (MILP) Table 7. Modeling approach and objective-Part b.…”

“…The energy applications are divided into: biofuel [50,53,85,88,90,94], biomass based power [55], electricity [63][64][65], and hydrogen [92]. For the case of food industry there are general cases without any specific product [77]; dairy [61,62,98]; food grain [59,60]; meat [56]; sugar beet [51]; and wheat [54].…”

“…This SDG aims to expand the infrastructure and update the technology to provide clean, efficient, and reliable energy. Resilient and sustainable supply chain designs for electricity and fuels assist in this purpose[50,53,55,[63][64][65]85,88,90,92,94]. • Goal 8: Decent work and economic growth This goal wants to achieve full and productive employment and decent work.…”

A Robust Possibilistic Bi-Objective Mixed Integer Model for Green Biofuel Supply Chain Design under Uncertain Conditions

“…Zhang et al [27] tailored an integrated approach based on a mixedinteger linear programming model of geographical information system and sustainability approach to economic optimization of the dual-feedstock lignocellulosic supply chain by soil carbon stock and greenhouse gas emission considerations. Meanwhile, Ghadimi et al [28] manipulated a new multi-echelon MSW-to-biofuel supply chain network design based on a sustainable cross-efficiency DEA model to find the most suitable location of facilities. Zarei et al [29] presented a multi-period, multi-feedstocks MIP model to design second-and third-generation BSCN to minimize total costs.…”

A novel ranking approach with common weights: An implementation in the presence of interval data and flexible measures

“…He exploited the property of multiple weighting schemes generated over the cross evaluation process in developing a methodology that yields not only robust ranking patterns but also more realistic sets of weights for the DMUs. Also see [45], [23], [25], and etc. Now, we focus on some methodologies more relevant to our study, in the followings:…”