A multi-agency coordination resource allocation and routing decision-making problem: A coordinated truck-and-drone DSS for improved wildfire detection coverage

This study proposes a novel coordinated truck-drone system as a decision support system to improve fire suppression operations. The problem in this study is formulated as a bi-objective mathematical model to minimize total monitoring cost and time, considering trucks as mobile drone depots and that drones can fly at various altitudes and access hard-to-reach areas. In addition, time and cost parameters in the mathematical model are deemed uncertain, rendering the model more realistic. Accelerated Benders' decomposition (ABD) is utilized to solve the model rapidly. Furthermore, a column-and-constraint generation (CCG) algorithm is employed, which is an effective method for solving scenario-based models under uncertainty. Two criteria are subsequently used to evaluate and compare proposed robust optimization approaches (risk averse and min-max relative regret). The results indicate that the proposed system can assist firefighters in determining the optimal number of drones and trucks to patrol and the time and cost required to visit all areas. Moreover, the findings demonstrate that the min-max relative regret approach can outperform other methods during the hot season when the risk of wildfire is elevated. In contrast, when the fire risk is lower during the cold season, time and cost can be effectively managed, and a risk-averse strategy can be implemented. Finally, the proposed solution framework can facilitate optimal strategic organizational decision planning.