A comparison of methods for determining performance based employee deployment in production systems

Abstract

Employee deployment is a crucial process in production systems. Based on qualification and individual performance of employees, deployment decisions can lead to ambiguous outcomes. This paper first reviews the state of the art and further compares two methods based on combinatorial analysis for employee deployment. Therefore, this paper emphasizes the costs and benefits of a Brute Force and an alternative Greedy method. When considering the qualification and individual performance of each employee, both algorithms provide working solutions. In direct comparison, the outcome of the alternative Greedy algorithm is more efficient in terms of calculation time whereas the Brute Force method provides the combination with the global optimum. This means calculation time as well as quality of outcome differ. The exponential growth of employee allocation possibilities depends on the amount of employees and leads to high calculation times, when using a Brute Force method. The comparison of both methods reveal that the proposed alternative Greedy algorithm reaches nearly as high outcomes as the Brute Force does, with significantly less calculation time. Furthermore, this paper offers an insight into the impact of deployment decisions within production systems.