A cooperative covering model for optimizing critical supply networks under disruption risks

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.