Abstract | ||
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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.
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Year | DOI | Venue |
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2018 | 10.1145/3219819.3220056 | KDD |
Field | DocType | ISBN |
Salary,Computer science,Outsourcing,Algorithm | Conference | 978-1-4503-5552-0 |
Citations | PageRank | References |
0 | 0.34 | 18 |
Authors | ||
5 |
Name | Order | Citations | PageRank |
---|---|---|---|
Aris Anagnostopoulos | 1 | 1054 | 67.08 |
Carlos Castillo | 2 | 5033 | 246.57 |
Adriano Fazzone | 3 | 12 | 2.88 |
Stefano Leonardi | 4 | 411 | 28.87 |
Evimaria Terzi | 5 | 1580 | 83.54 |