|Aris Anagnostopoulos||Sapienza University of Rome|
|Carlos Castillo||Universitat Pompeu Fabra|
|Adriano Fazzone||Sapienza University of Rome|
|Stefano Leonardi||Sapienza University of Rome|
|Evimaria Terzi||Boston University|
In this paper, the authors provide algorithms for outsourcing and hiring workers in a general setting, where workers form a team and contribute different skills to perform a task. They call this model team formation with outsourcing.
Although freelancing work has grown substantially in recent years, in part facilitated by a number of online labor marketplaces, %(e.g., Guru, Freelancer, Amazon Mechanical Turk), traditional forms of “in-sourcing” work continue being the dominant form of employment. % in most companies. 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. We call this model team formation with outsourcing. In our model, tasks arrive in an online fashion: neither the number nor the composition of the tasks are known a-priori. At any point in time, there is a team of hired workers who receive a fixed salary independently of the work they perform. This team is dynamic: new members can be hired and existing members can be fired, at some cost. Additionally, some parts of the arriving tasks can be outsourced and thus completed by non-team members, at a premium. Our contribution is an efficient online cost-minimizing algorithm for hiring and firing team members and outsourcing tasks. We present theoretical bounds obtained using a primal—dual scheme proving that our algorithms have logarithmic competitive approximation ratio. We complement these results with experiments using semi-synthetic datasets based on actual task requirements and worker skills from three large online labor marketplaces.