Approximation algorithms for stochastic orienteering

In the Stochastic Orienteering problem, we are given a metric, where each node also has a job located there with some deterministic reward and a random size. (Think of the jobs as being chores one needs to run, and the sizes as the amount of time it takes to do the chore.) The goal is to adaptively decide which nodes to visit to maximize total expected reward, subject to the constraint that the total distance traveled plus the total size of jobs processed is at most a given budget of B. (I.e., we get reward for all those chores we finish by the end of the day). The (random) size of a job is not known until it is completely processed. Hence the problem combines aspects of both the stochastic knapsack problem with uncertain item sizes and the deterministic orienteering problem of using a limited travel time to maximize gathered rewards located at nodes. In this paper, we present a constant-factor approximation algorithm for the best non-adaptive policy for the Stochastic Orienteering p...

• Type: Journal
• Year: 2012
• Where: SODA
• Authors: Anupam Gupta, Ravishankar Krishnaswamy, Viswanath Nagarajan, R. Ravi
• Keywords: Adaptive Problem, Algorithms, Approximation Algorithm, Random Size, SODA 2012