On the quickest evacuation location problem in humanitarian operations: multi-objective modelling and matheuristic solution
提出最快疏散选址问题（QELP），将最快流问题与离散设施选址结合，建立多目标混合整数规划模型，最小化疏散时间、预算并平衡避难所利用率，采用鲁棒增强ε约束法和原创数学启发式求解，在现实规模实例上表现良好。
• Quickest Evacuation Location Problem: a novel optimisation model for concurrent shelter location and evacuation planning • QELP merges Facility Location problems and Quickest Flows on dynamic networks • Multiple objectives minimising evacuation makespan, minimising budget and balancing the utilisation rate of active shelters • A tailored implementation of the robust version of the Augmented epsilon-constraint method is adopted to test the model • An original Matheuristic is designed and implemented and demonstrates high performance on realistic large-scale instances Effective evacuation planning is critical in humanitarian operations to save lives. This paper introduces the Quickest Evacuation Location Problem (QELP), a novel optimisation model that combines the quickest flow problem with discrete facility location to support humanitarian operations. Its scope falls into the field of enhancing evacuation planning and design by identifying, among a finite set of candidates, the set of shelters that would allow the quickest possible evacuation process. To secure flexible and realistic decision support, a multi-objective mixed integer programming model is developed, aiming to minimise the evacuation makespan and the total budget required to install and operate the shelters, while also balancing the utilisation rate of activated shelters. The Robust Augmented ε -constraint method is adopted as a solution scheme, and it is successfully combined with an original Matheuristic approach to boost its performance while approximating the Pareto Set on increasing-size networks. Despite the complexity of time-expanded networks, experiments on realistic instances demonstrate scalable performance and clear trade-offs among the three objectives, confirming the suitability of the QELP in providing decision-makers with valuable support for real-world planning processes in humanitarian operations.