Area-weighted resource allocation for humanitarian logistics: An enhanced proximal policy optimization approach

This paper addresses the critical challenge of resource allocation in humanitarian logistics during disaster response. We develop a comprehensive framework that optimizes the distribution of limited relief resources across multiple affected areas while balancing efficiency, effectiveness, and equity objectives. The novel contribution is the introduction of area weights into the optimization model, enabling prioritization based on region-specific factors such as population density, infrastructure importance, and proportion of vulnerable populations. We formulate the problem as a Markov Decision Process and propose an enhanced Proximal Policy Optimization (PPO) algorithm with an Actor-Critic architecture to solve the resulting nonlinear optimization model. Our approach focuses specifically on the critical 72-hour “golden rescue period” after disasters occur, when resource allocation decisions have the most significant impact on saving lives. The PPO algorithm efficiently navigates the complex trade-offs between competing objectives while accounting for capacity constraints and dynamic state transitions over multiple time periods. Numerical experiments demonstrate that our approach achieves significant improvements in convergence speed, solution quality, and stability across various problem scales. The model effectively adapts to different objective priorities and maintains robust performance under demand uncertainty, making it well-suited for complex, dynamic humanitarian operations. This research provides a practical solution for resource allocation decisions in disaster response scenarios, contributing to the development of more effective humanitarian logistics systems that can better serve affected populations during crises.