A Semiautomated Human Resource Management System

Abstract

This paper is an extension of our previous work where we have introduced a skill-based mathematical model of resource allocation. This paper expands our skill based approach by introducing adaptive skill sets for employees and a history-based initial evaluation strategy. For this purpose, the mathematical model is adjusted in order to modify skill vectors after a task allocation. In turn this enables estimations of the time to task completion based on employee history. We experimentally evaluate the impact of the skill adjustment on the project duration and cost in an agent-based simulation environment. The conclusion of the experiment is that taking into account the implicit skill gain of employees during their daily activity decreases projected costs and execution time significantly, which is this papers contribution to the state of the art. This approach is a good way to keep the teams skill sets automatically updated. The experiment is designed as an agent society simulation and through their interactions raw data is collected in order to calculate the performance measures. A scalability experiment is also presented showing slight (1%) decrease in project duration when tasks double while costs decrease between 7–32 %.