On average there are over 500 forest fires burning more than 3,700 ha each year in South Korea. Between 2014 and 2018, 62 percent of forest fires were multiple fires burning simultaneously in more than two different locations across the country. These multiple fires make it difficult to make decisions of resource dispatch for suppression, as they compete for limited resources, such as helicopters. In order to support and improve decision-making in real-time helicopter dispatch, we developed a conceptual decision framework that lays out the process of information gathering, prioritization, and problem-solving. We developed an integer linear programming (ILP) model to solve helicopter dispatch problems. The model uses real-time data on available helicopter resources and fire spread to develop dispatch decisions while minimizing both suppression costs and burn perimeters. We developed five hypothetical forest fire events and applied the model to demonstrate the utility of the optimization model. We compared the model solutions with the manual solutions obtained from the Korea Forest Service for model validation. Our results show that the optimization model can rapidly analyze various fire suppression scenarios and provide a wide range of helicopter dispatch solution options. This can be useful to decision-makers as it can provide insights about decision sensitivity and bounds. The decision model can provide an analytical tool to assist decision-makers in determining suppression objectives, prioritizing resource allocations, and developing effective and efficient fire suppression solutions.