Abstract
In this paper we provide methods for the Continuous Monitoring Problem with Inter-Depot routes (CMPID). It arises when a number of agents or vehicles have to persistently survey a set of locations. Each agent has limited energy storage (e.g., fuel tank or battery capacity) and can renew this resource at any available base station. Various real-world scenarios could be modeled with this formulation. In this paper we consider the application of this problem to disaster response management, where wide area surveillance is performed by unmanned aerial vehicles. We propose a new method based on the Insertion Heuristic and the metaheuristic Variable Neighborhood Search. The proposed algorithm computes solutions for large real-life scenarios in a few seconds and iteratively improves them. Solutions obtained on small instances (where the optimum could be computed) are on average 2.6% far from optimum. Furthermore, the proposed algorithm outperforms existing methods for the Continuous Monitoring Problem (CMP) in both solution quality (in 3 times) and computational time (more than 400 times faster).
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Mersheeva, V., Friedrich, G. (2013). Variable Neighborhood Search for Continuous Monitoring Problem with Inter-Depot Routes. In: Timm, I.J., Thimm, M. (eds) KI 2013: Advances in Artificial Intelligence. KI 2013. Lecture Notes in Computer Science(), vol 8077. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40942-4_10
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DOI: https://doi.org/10.1007/978-3-642-40942-4_10
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