# An integrated multi-criteria decision-making model for long-term planning of UAVs in disaster management

## TL;DR

This paper introduces a new model for planning UAV base locations and missions in disaster management using decision-making techniques to optimize cost and efficiency.

## Contribution

The novel contribution is an integrated multi-criteria decision-making model combining AHP and goal programming for UAV base and mission planning.

## Key findings

- The model achieved cost improvements of up to 14.8% in a real-world earthquake scenario.
- Distance improvements of up to 8.57% were observed in test scenarios.
- Algorithms were developed to refine goal targets and optimize UAV deployment.

## Abstract

With their superior capabilities, unmanned aerial vehicles (UAVs) play a crucial role in search, rescue, and surveillance operations in disaster management. It is of great importance in the long run to optimally designate the base locations and deployment plans of the UAVs needing a base for their operations. In this study, we develop an integrated multi-criteria decision-making model to select bases and plan missions of UAVs using a combination of multi-attribute and multi-objective optimization techniques, with the decision maker having an interactive role. We formulate a goal programming model in which the number of bases, flight distance, unairworthy days, and cost are jointly minimized. The Analytic Hierarchy Process (AHP) is used to designate the associated goal weights. We develop Algorithm 1 to identify the target level for each goal and Algorithm 2 to refine the model for better solutions. We apply the process in a problem setting where designated disaster activity zones (DAZs) need to be covered by some candidate bases, among which an optimal selection is made. The model’s validation and refinement were evaluated across multiple scenarios. The illustrative example yields improvements of 8.57% in cost in the first scenario and 7.54% in distance in the second. The third scenario achieves 7.66% and 6.58% improvements in distance and cost, respectively. A real-world earthquake scenario from Türkiye further demonstrates the model’s practical applicability, with 5% improvement in distance and 14.8% in cost. The results of the proposed decision-making process guarantee satisfactory solutions for long-term base and operational planning of UAVs.