In the Stochastic Orienteering problem, we are given a finite metric space, where each node contains a job with some deterministic reward and a random processing time. The processing time distributions are known and independent across nodes. However the actual processing time of a job is not known until it is completely processed. The objective is to compute a non-anticipatory policy to visit nodes (and run the corresponding jobs) so as to maximize the total expected reward, subject to the total distance traveled plus the total processing time being at most a given budget of B. This problem combines aspects of the stochastic knapsack problem with uncertain item sizes [18] , as well as the deterministic orienteering problem [8] . In this paper, we consider both non-adaptive and adaptive policies for Stochastic Orienteering. We present a constantfactor approximation algorithm for the non-adaptive version, and an O(log log B)-approximation algorithm for the adaptive version. We extend both these results to directed metrics and a more general sequence orienteering problem. Finally, we address the Stochastic Orienteering problem when the node rewards are also random and possibly correlated with the processing time, and obtain an O(log n log B)-approximation algorithm; here n is the number of nodes in the metric. All our results for adaptive policies also bound the corresponding "adaptivity gaps".
Gupta et al. REF designed an O(1)-approximation algorithm for the best non-adaptive policy for the stochastic orienteering problem.
Running Errands in Time: Approximation Algorithms for Stochastic Orienteering
Math. Oper. Res.
Math. Oper. Res.
Mathematics Computer Science