Although freelancing work has grown substantially in recent years, in part facilitated by a number of online labor marketplaces, traditional forms of “in-sourcing” work continue being the 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. 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.
2018, KDD '18 The 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Pages 1109-1118
Algorithms for hiring and outsourcing in the online labor market (04b Atto di convegno in volume)
Anagnostopoulos Aris, Castillo Carlos, Fazzone Adriano, Leonardi Stefano, Terzi Evimaria
Gruppo di ricerca: Algorithms and Data Science