Multi-objective optimization for post-earthquake disaster waste management: A case study in Türkiye

Sustainable disaster waste management (DWM) plays a critical role in improving resource efficiency and reducing environmental and socio-economic impacts during post-disaster recovery. Unlike conventional solid waste systems, post-earthquake debris arrives at temporary sites as mixed, unsorted material, requiring on-site classification before transport to appropriate treatment or disposal facilities. To address this challenge, this study proposes a multi-objective mixed-integer linear programming (MILP) model that captures three integrated stages: mixed waste collection from disaster zones, waste sorting and separation at temporary waste management sites (TWSs), and classified transportation of waste streams to terminal facilities such as cement plants, iron–steel factories, and landfills. In contrast to conventional transfer site models that only perform transshipment, the proposed TWS structure integrates waste classification, facility location, and transportation decisions within an integrated optimization framework. The model simultaneously optimizes four sustainability objectives: total economic cost, carbon emissions, psychological impact on nearby populations, and TWS suitability. Five multi-objective decision-making (MODM) methods are applied, and the most appropriate approach is selected using the displaced ideal solution (DIS) method. The proposed framework is validated through a case study of three provinces heavily affected by the February 6, 2023 earthquake in Türkiye, accompanied by sensitivity analyses. The findings provide a comprehensive and sustainability-oriented decision-support framework for post-earthquake DWM, improving economic, environmental, and social performance during recovery operations.