An Improved Approximation Algorithm for Wage Determination and Online Task Allocation in Crowd-Sourcing

Crowd-sourcing has attracted much attention due to its growing importance society, and numerous studies have been conducted on task allocation wage determination. Recent works focused optimizing workers' wages, simultaneously. However, existing methods do not provide good solutions for real-world crowd-sourcing platforms the low approximation ratio or myopic problem settings. We tackle an optimization determination online in propose a fast 1-1/(k+3)^(1/2)-approximation algorithm, where k is minimum of tasks' budgets (numbers possible assignments). This greater than equal method. The proposed method reduces tackled non-convex multi-period continuous by approximating objective function. Then, transforms reduced into convex cost flow problem, which well-known combinatorial solves it capacity scaling algorithm. Synthetic experiments simulation using real data show that faster outputs higher values methods.