Dynamic Vehicle Location and Relocation Under Supply and Demand Uncertainties in Disaster Response

The unpredictable nature of disasters creates uncertainty in rescue demands, while their large-scale impact necessitates the use of volunteer vehicles, leading to an uncertain supply of emergency vehicle resources. This imbalance between available emergency vehicles and actual rescue needs can result in delayed rescue efforts, unmet demands, and wasted vehicle resources in regions with surplus vehicles. However, current research predominantly focuses on efficient deployment and dispatch of pre-positioned vehicles, overlooking the spatiotemporal imbalance between vehicle supply and uncertain demands, as well as the effective utilization of volunteer vehicles. To tackle these issues, this study proposes an adaptive approach for dispatching and relocating heterogeneous vehicles—including both pre-positioned and volunteer units—while strategically locating temporary facilities to enhance response efficiency. The problem is formulated as an extension of the Location Routing Problem (LRP), incorporating multiple visits and open routes. A rolling horizon-based dynamic solution framework is developed to update plans as new information becomes available and manage uncertainties. Experimental results reveal that: (1) In small-scale scenarios, the proposed strategy underperforms compared to traditional methods due to increased scheduling complexity and resource dispersion. Conversely, in larger-scale scenarios, it enhances response efficiency by reducing vehicle idle distances through temporary facility locations. (2) Vehicle relocation between permanent facilities proves ineffective in disaster response. When vehicles are scarce, they are fully utilized to meet demands, and when abundant, relocations become redundant. In contrast, the introduction of temporary facilities helps bridge the spatiotemporal imbalance between vehicle supply and demand, ultimately enhancing overall response efficiency.

Jul 23, 2025 1:00 PM — 1:15 PM

2025 INFORMS International Meeting