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Darvishi H, Rezaeian J, Shirazi B, Mahdavi I. A Multi-objective Optimization Model for Post-natural Disaster Waste Clean-Up with Multimodal Transport. Disaster Prev. Manag. Know. 2026; 15 (4) :462-509
URL: http://dpmk.ir/article-1-791-en.html

Background and objective Natural disasters result in the generation of massive volumes of waste, leading to severe environmental, economic, and social problems. Inefficient waste management can delay the recovery efforts and incur additional costs. This study aims to develop a multi-objective optimization (MOO) model for post-disaster waste cleanup, employing a multimodal transportation approach (truck/train) and risk prioritization of affected areas.
Method In this study, the affected areas were first ranked using the analytic hierarchy process (AHP) based on five risk criteria (life risk, environmental pollution, psychological impacts, economic disruption, and risks from disaster waste). Then, the MOO model was implemented using the epsilon-constraint method across seven defined problems with varying scales. The scenarios were applied to the flooded rural areas of Gorgan city and Aqqala county in Golestan province, Iran. The model considered the minimization of the time for the completion of debris removal and waste transportation operations, as well as the reduction of transportation and equipment costs (for train stations and temporary waste management sites), as two objective functions.
Results As the problem scale increased, computation time also increased, with the model exhibiting NP-Hard characteristics at larger scales. The obtained Pareto points confirmed the trade-off between time minimization and cost reduction objectives, highlighting the need for selections aligned with operational policies. Furthermore, multimodal transportation, compared with road-only mode, resulted in a 30.1% reduction in operating time and an 11.3% reduction in total costs. Further analysis revealed that road transport performed better over short distances between the affected areas and final disposal sites, whereas multimodal transport was superior over longer distances. Risk prioritization results demonstrated a targeted resource allocation to high-risk areas.
Conclusion The presented model can serve as a practical tool for crisis managers in planning post-disaster waste clean-up. Employing multimodal transport alongside a multi-criteria decision-making approach not only enhances decision-making efficiency and quality but also enables optimal resource allocation to high-risk regions.