Abstract
In the Stochastic Orienteering Problem (SOP), we are given finite metric space (V, d) and a starting point \(\rho \in V\). Each \(v \in V\) has an associated reward \(r_v \ge 0\) and random completion time \(S_v\), where the distribution of each \(S_v\) is know (once a reward has been earned, it cannot be earned again); the time cost of traveling from \(u \in V\) to \(v \in V\) is d(u, v). The goal is to sequentially visit vertices and complete tasks in order to maximize the total rewards within 1 unit of time (after normalization). In this paper, we present a nonadaptive \(O(\epsilon ^{-14})\)-approximation for (the original, adaptive) SOP when we relax the unit time budget to \((1+\epsilon )\), \(0< \epsilon < 1\).
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Jiang, Y. (2017). Constant Approximation for Stochastic Orienteering Problem with \((1+\epsilon )\)-Budget Relaxiation. In: Cao, Y., Chen, J. (eds) Computing and Combinatorics. COCOON 2017. Lecture Notes in Computer Science(), vol 10392. Springer, Cham. https://doi.org/10.1007/978-3-319-62389-4_25
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DOI: https://doi.org/10.1007/978-3-319-62389-4_25
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