Abstract

In this work, we investigate the problem of mobile crowd-sourcing, where workers are financially motivated to perfor-m location-based tasks physically. Unlike current industry practice that relies on workers to manually browse and fil-ter tasks to perform, we intend to automatically make task recommendations based on workers ’ historical trajectories and desired time budgets. However, predicting workers ’ tra-jectories is inevitably faced with uncertainties, as no one will take exactly the same route every day; yet such uncer-tainties are oftentimes abstracted away in the known litera-ture. In this work, we depart from the deterministic model-ing and study the stochastic task recommendation problem where each worker is associated with several predicted rou-tine routes with probabilities. We formulate this problem as a stochastic integer linear program whose goal is to maximize the expected total utility achieved by all workers. We fur-ther exploit the separable structure of the formulation and apply the Lagrangian relaxation technique to scale up the solution approach. Experiments have been performed over the instances generated using the real Singapore transporta-tion network. The results show that we can find significantly better solutions than the deterministic formulation.