A multi-objective robust possibilistic programming approach for sustainable disaster waste management under disruptions and uncertainties

Disasters can generate huge amounts of waste, based on their nature and intensity. The debris could overburden existing waste processing facilities, disrupting response and recovery phase efforts. If disaster waste is not properly managed, it can have serious public health and environmental consequences. This research develops a two-stage framework to assist disaster waste managers in making effective decisions in a post-disaster environment. The proposed framework's first stage efficiently assesses disruption risk and allocates waste to temporary waste management sites in a cost-effective manner during the response phase, while the second stage assists in waste processing following the triple lines of sustainability: economy, environment, and society during the long-term recovery phase. Further, the chaotic environment of a disaster makes it difficult to access information, compromising the effectiveness of waste management decisions; thus, a Me measure-based robust possibilistic programming solution methodology has been developed. A case study is used to validate the suggested framework and solution approach. The findings show that by incurring 15.1% greater costs, desired level of social and environmental goals may be attained. Further, it is found that the higher priority of economic objective makes a minimal number of waste processing facilities operational while the high priority of social objective makes maximum facilities operational which exponentially increases the total cost of waste management. The proposed framework will assist policymakers in developing tactical and strategic plans for disaster waste management.