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

In disaster preparedness, strategically placing relief supplies is crucial to guarantee time-ly and adequate relief efforts. Important decisions in this process include determiningoptimal warehouse locations, assessing logistical resources, and strategically allocatingcritical supplies to distribution points. The inherent uncertainty surrounding a potentialdisaster amplifies the complexity of these decisions. We formulate a scenario-based multi-objective optimization model that integrates the advanced placement and allocation ofrelief supplies, extending the general form of a cooperative covering location problem.The proposed model maximizes demand coverage while minimizing underlying storageand logistics costs. Furthermore, the model accounts for diverse disruption scenarios us-ing stochastic programming, treating the disaster impact of individual factors as randomvariables. Given large-scale disaster situations, our model evaluates the effect of potentialdisruptions, enabling the assessment of optimal network solutions. Based on an empiricalcase study focusing on the German national food stockpiling system, we demonstrate thefeasibility of the introduced methodology in developing efficient stockpiling and prepared-ness strategies while facilitating the identification of vulnerabilities to enhance overallresilience. ... mehrOur results show that incorporating stochastic factors, such as warehouse avail-ability and operability, route failure, coverage radius limitations, and demand volatility,can significantly impact the optimal network configuration and influence demand coverageand total costs. Furthermore, the methodology proves to be both scalable and feasiblewhen applied to a large-scale scenario.