A data-driven hybrid scenario-based robust optimization method for relief logistics network design
Journal
Transportation Research Part E: Logistics and Transportation Review
Journal Volume
194
Start Page
103931
ISSN
1366-5545
Date Issued
2025-02
Author(s)
Amin Amani, Mohammad
Asumadu Sarkodie, Samuel
Mahdi Nasiri, Mohammad
Tavakkoli-Moghaddam, Reza
Abstract
The incorporation of artificial intelligence (AI) and robust optimization methods for the planning and design of relief logistics networks under relief demand–supply uncertainty appears promising for intelligent disaster management (IDM). This research proposes a data-driven hybrid scenario-based robust (SBR) method for a mixed integer second-order cone programming (MISOCP) model that integrates machine learning with a hybrid robust optimization approach to address the above issue. A machine learning technique is utilized to cluster the casualties based on location coordinates and injury severity score. Moreover, the hybrid SBR optimization method and robust optimization based on the uncertainty sets technique are utilized to cope with uncertain parameters such as the probability of facility disruption, the number of wounded individuals, transportation time, and relief demand. Additionally, the epsilon-constraint technique is applied to seek the solution for the bi-objective model. Focusing on a real case (the Kermanshah disaster), our analytical results have demonstrated not only the validity but also the relative merits of the proposed methodology against typical stochastic and robust optimization approaches. Besides, the proposed method shows all casualties can be efficiently transported to receive medical services at a fair cost, which is crucial for disaster management.
Subjects
Facility disruption
Humanitarian relief logistics
Intelligent disaster management
Machine learning
Robust optimization
SDGs
Publisher
Elsevier BV
Type
journal article