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Although freelancing work has grown substantially in recent years, in partfacilitated by a number of online labor marketplaces, (e.g., Guru, Freelancer,Amazon Mechanical Turk), traditional forms of "in-sourcing" work continue beingthe dominant form of employment. This means that, at least for the time being,freelancing and salaried employment will continue to co-exist. In this paper,we provide algorithms for outsourcing and hiring workers in a general setting,where workers form a team and contribute different skills to perform a task. Wecall this model team formation with outsourcing. In our model, tasks arrive inan online fashion: neither the number nor the composition of the tasks is knowna-priori. At any point in time, there is a team of hired workers who receive afixed salary independently of the work they perform. This team is dynamic: newmembers can be hired and existing members can be fired, at some cost.Additionally, some parts of the arriving tasks can be outsourced and thuscompleted by non-team members, at a premium. Our contribution is an efficientonline cost-minimizing algorithm for hiring and firing team members andoutsourcing tasks. We present theoretical bounds obtained using a primal-dualscheme proving that our algorithms have a logarithmic competitive approximationratio. We complement these results with experiments using semi-syntheticdatasets based on actual task requirements and worker skills from three largeonline labor marketplaces.

Algorithms for Hiring and Outsourcing in the Online Labor Market