SHERP

Optimal search-relocation trade-off in Markovian-target searching

Cited 0 time in webofscience Cited 11 time in scopus
Authors
Hong, Sung-Pil; Cho, Sung-Jin; Park, Myoung-Ju; Lee, Moon-Gul
Issue Date
2008-07-20
Publisher
Elsevier
Citation
Computers & Operations Research 2009;36:2097-2104
Keywords
Optimization; Search; Networks and graphs; Probability
Abstract
In this study, a standard moving-target search model was extended with a multiple-search-speed option, whereby a trade-off is enabled between the increased detection chances owing to the searcher's better location and the increased uncertainty of the target's location resulting from the diminished search performance incurred in the relocation. This enhances the detection probability of the output search path and, thereby, the model's practicality. However, the scalability of the solution method is essential to its implementation, as the basic model is already NP-hard. We developed an efficient heuristic by combining the idea of approximate nondetection probability minimization and a hybridized shortest-path heuristic that exploits the fast-mixing property of the Markov chain. According to the results of an intensive experiment, the heuristic achieves a near-optimal trade-off within a very reasonable computation time.
ISSN
0305-0548
Language
English
URI
http://hdl.handle.net/10371/5348
DOI
https://doi.org/10.1016/j.cor.2008.07.007
https://doi.org/10.1016/j.cor.2008.07.007
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College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Industrial Engineering (산업공학과)Journal Papers (저널논문_산업공학과)
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