A pseudo-polynomial heuristic for path-constrained discrete-time Markovian-target search

Sung-Pil Hong, Sung-Jin Cho and Myoung-Ju Park

European Journal of Operational Research, 2009, vol. 193, issue 2, 351-364

Abstract: We propose a new heuristic for the single-searcher path-constrained discrete-time Markovian-target search. The algorithm minimizes an approximate, instead of exact, nondetection probability computed from the conditional probability that reflects the search history over the time windows of a fixed length, l. Having a pseudo-polynomial complexity, it can solve, in reasonable time, the instances an order of magnitude larger than those solved in the previous studies. By an asymptotic analysis relying on the fast-mixing Markov chain, we show that the relative error of the approximation exponentially diminishes as l increases and the experimental results confirm the analysis. The experiment also reveals a correlation very close to 1 between the approximate and exact nondetection probability of a search path. This means that the heuristic produces near-optimal search paths.

Keywords: Search; theory; Heuristics; Markov; processes; Network; flows (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:193:y:2009:i:2:p:351-364

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