The financial impact of human resources configuration: A quantitative analysis based on modified single candidate optimizer

Abstract

Recently, by complicated and fast changing business environments, the effective allocation of Human Resources (HR) is considered as an important task to achieve success within organizations. However, the optimal allocation of HR is considered as a complicated challenge due to the uncertainties that are inherent in the process. Traditional approaches often rely on manual decision-making, which can result in less effective allocations and reduced productivity. With the rise of big data and advanced analytics, there is an increasing demand for data-driven methodologies to enhance HR allocation. This paper presents an innovative HR optimization framework that uses a modified metaheuristic model, called the Modified Single Candidate Optimizer (MSCO) algorithm to resolve this task. The framework integrates big data analytics and system analysis to establish a quantitative management strategy for optimizing HR configurations. By using the advantages of the proposed MSCO, the framework can effectively address the HR allocation problems to provide an optimal solution. The results indicate that the proposed framework significantly improves HR utilization rates, labor productivity.

Keywords

• Enterprise human resource optimization
• Financial
• Metaheuristic algorithm
• Modified Single Candidate Optimizer (MSCO)
• Big data
• Complex business environment