Worker Selection in Crowd-sourced Platforms using Non-dominated Sorting

Crowdsourcing has lead to a paradigm shift in the manner commercial houses execute projects by lowering the cost-per-unit of production. A crucial aspect in crowdsourcing is selecting the best set of workers that can perform a task. The environment envisaged in this work is an independent pool of workers, each equipped with a pre-defined set of skills. We assume that these skills do not follow any priority order over each other. Given a task with a set of required skills, our aim is to perform a non-dominated sorting of the workers based on the requirement. From this set of ordered workers, we use domination count to select the best set of workers that can perform the task. Empirical results using real dataset is presented.