Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/336832 
Year of Publication: 
2025
Citation: 
[Journal:] Annals of Operations Research [ISSN:] 1572-9338 [Volume:] 357 [Issue:] 2-3 [Publisher:] Springer US [Place:] New York, NY [Year:] 2025 [Pages:] 909-944
Publisher: 
Springer US, New York, NY
Abstract: 
In disaster preparedness, strategically placing relief supplies is crucial to guarantee timely and adequate relief efforts. Important decisions in this process include determining optimal warehouse locations, assessing logistical resources, and strategically allocating critical supplies to distribution points. The inherent uncertainty surrounding a potential disaster amplifies the complexity of these decisions. We formulate a scenario-based multi-objective optimization model that integrates the advanced placement and allocation of relief supplies, extending the general form of a cooperative covering location problem. The proposed model maximizes demand coverage while minimizing underlying storage and logistics costs. Furthermore, the model accounts for diverse disruption scenarios using stochastic programming, treating the disaster impact of individual factors as random variables. Given large-scale disaster situations, our model evaluates the effect of potential disruptions, enabling the assessment of optimal network solutions. Based on an empirical case study focusing on the German national food stockpiling system, we demonstrate the feasibility of the introduced methodology in developing efficient stockpiling and preparedness strategies while facilitating the identification of vulnerabilities to enhance overall resilience. Our results show that incorporating stochastic factors, such as warehouse availability and operability, route failure, coverage radius limitations, and demand volatility, can significantly impact the optimal network configuration and influence demand coverage and total costs. Furthermore, the methodology proves to be both scalable and feasible when applied to a large-scale scenario.
Subjects: 
Network analysis
Disaster preparedness
Location problem
Supply chain resilience
Uncertainty
Persistent Identifier of the first edition: 
Creative Commons License: 
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Document Type: 
Article
Document Version: 
Published Version

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