Robust Optimization of Emergency Transportation Routes under Uncertainty Using Grey Wolf Optimizer

In emergencies, the demand for critical supplies often rises sharply and unpredictably, making rapid and reliable delivery essential to mitigate further harm. However, these demands are inherently uncertain and can fluctuate over time, making effective planning complicated. To address this, we propose a multi-objective robust optimization framework for emergency transportation that aims to minimize total travel time, reduce unmet demand, limit the maximum operational time for each truck or Unmanned Aerial Vehicle (UAV), and ensure route effectiveness even under worst-case scenarios. Employing a budget-of-uncertainty approach, the model preserves solution reliability despite severe disruptions. A tailored Grey Wolf Optimizer (GWO) is integrated to efficiently solve this complex problem. Computational experiments on realistic scenarios demonstrate that the framework can substantially reduce both unmet demand and total operational time. Looking ahead, incorporating live data for dynamic re-routing, exploring multi- depot and multi-echelon configurations, and accounting for UAV energy constraints could further enhance the practicality and sustainability of this approach for emergency logistics planning

کلیدواژه ها:

Emergency logistics ، robust optimization ، Grey Wolf Optimizer ، UAV routing ، uncertainty ، multi-objective optimization