A novel methodology to select sustainable municipal solid waste management scenarios from three-way decisions perspective

Abstract

Selecting a sustainable municipal solid waste management (MSWM) scenario can reduce greenhouse gas emissions and deal with the energy crisis, effectively. However, traditional decision approaches only rank alternative scenarios but fail to manage them. Implementing effective management for alternative MSWM scenarios can save decision costs and improve efficiency. Thus, this paper aims to construct a novel multi-criteria group decision-making (MCGDM) methodology to manage and rank sustainable scenarios. To evaluate sustainable MSWM scenarios, a comprehensive evaluation index system is constructed from five dimensions including economy, environment, society, technology, and energy. The hesitant fuzzy linguistic term set is employed to articulate evaluation information. Given the importance of experts and the incomplete criteria weight information, an inclusion measure-based approach is proposed to obtain expert weights, and the grey relational analysis linear programming model with incomplete criteria weight information is constructed to determine criteria weights. An inclusion measure-based hesitant fuzzy linguistic multigranulation three-way decisions over two universes approach is developed to manage and rank alternative MSWM scenarios. The feasibility of the proposed methodology is illustrated by a numerical case. Triple sensitivity analysis is performed from the perspectives of decision-makers’ risk appetite, expert weight fluctuations, and criteria weight fluctuations. A comparative analysis is implemented to demonstrate the effectiveness and superiority of the proposed methodology. This study provides a novel insight to solve MSWM scenario selection and can extensively be applied to handle other MCGDM problems. • Constructing a comprehensive evaluation index system from multiple dimensions. • Describing evaluation criteria by hesitant fuzzy linguistic term set. • Proposing an inclusion measure-based approach to derive expert weights. • Employing a grey relational analysis linear programming model to determine criteria weights. • Developing an inclusion measure-based multigranulation three-way decisions to manage and rank all alternative scenarios.