Abstract
The firefighter problem (FFP) is used as a model to simulate how a fire breaks out and spreads to its surroundings over a discrete time period. The goal is to deploy a given number of firefighters on strategic points at each time step to contain the fire in a most efficient way, so that as many areas are saved from the fire as possible. In this paper we introduce a new solution representation for the FFP which can be applied in metaheuristic approaches. Compared to the existing approach in the literature, it is more compact in a sense that the solution space is smaller although the complexity for evaluating a solution remains unchanged. We use this representation in conjunction with a variable neighborhood search (VNS) approach to tackle the FFP. To speed up the optimization process, we propose an incremental evaluation technique that omits unnecessary re-calculations. Computational tests were performed on a benchmark instance set containing 120 random graphs of different size and density. Results indicate that our VNS approach is highly competitive with existing state-of-the-art approaches.
This work is supported by the Austrian Science Fund (FWF) grant P24660-N23.
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Hu, B., Windbichler, A., Raidl, G.R. (2015). A New Solution Representation for the Firefighter Problem. In: Ochoa, G., Chicano, F. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2015. Lecture Notes in Computer Science(), vol 9026. Springer, Cham. https://doi.org/10.1007/978-3-319-16468-7_3
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