A chance-constraint approach for optimizing Social Engagement-based services

Social Engagement is a novel business model transforming final users of a service from passive into active components. In this framework, people are contacted by a company and they are asked to perform tasks in exchange for a reward. This arises the complicated optimization problem of allocating the different types of workforce so as to minimize costs. We address this problem by explicitly modeling the behavior of contacted candidates through consolidated concepts from utility theory and proposing a chance-constrained optimization model aiming at optimally deciding which user to contact, the amount of the reward proposed, and how many employees to use in order to minimize the total expected costs of the operations. A solution approach is proposed and its computational efficiency is investigated through experiments.