Emergency logistics scheduling with multiple supply-demand points based on grey interval

This study aimed to address the problem of post-disaster emergency material dispatching from multiple supply points to multiple demand points. In large-scale natural disasters, it is very important for multiple emergency material supply points to serve as sources of materials for multiple disaster sites and to determine emergency material scheduling solutions accurately. Furthermore, the quantity of emergency materials required at each disaster site is uncertain. To address this issue, in this study, we developed an emergency material scheduling model with multiple logistics supply points for multiple demand points based on the grey interval numbers. To optimize the proposed multi-supply-point and multi-demand-point emergency material scheduling mode, a multi-objective optimization algorithm based on a genetic algorithm was used. Experimental results demonstrate that the multi-objective optimization method can solve the emergency logistics scheduling problem better than the particle swarm optimization multi-objective solution algorithm. Additionally, the multi-supply point and multi-demand point emergency material dispatch model and optimization algorithm provides robust support for emergency management system decision-makers when they need to respond quickly to disaster relief activities.