Productive and Profitable Cluster Hire

Cluster Hire is defined as a problem of hiring a group of experts to maximize profits with the ability to complete multiple projects simultaneously under a budget. It assumes that we have a set of projects which require skills and experts who possess various skills. The process of hiring a group of experts to complete a set of projects under the given conditions is proven to be the NP-hard problem. Individuals expect financial support (i.e.salary) which can be handled by a specific budget that we get, to work on the projects. Addition to maximizing the total profit, we are interested in hiring productive experts who can work many projects concurrently with effective result. Therefore, this paper examines the problem of hiring a cluster of experts, so that the total salary does not exceed more than a given budget and maximizes the total benefit of the projects that a highly productive team can cover collectively. We propose two greedy algorithms to solve this problem with different strategies. We illustrate the effectiveness of our approach by experimenting with the synthetic data sets. The results from a study of the synthetic dataset were compared with Bruteforce and Random Algorithm. It suggested that our proposed both project greedy and expert greedy algorithms performed well regarding both accuracy and run-time.