Application of the human resource allocation problem 4.0: a study case - Grillo Espinoza, Hanzel (2026)
In Industry 4.0, the allocation of human resources is challenged by the diversity, complexity, and cognitive load required by integrating cyber-physical systems. Digitalization, process control, information handling, and the growing need for human-machine collaboration are changing the tasks that people perform. The Human Resource Allocation Problem has evolved into Human Resource Allocation Problem 4.0 to adapt to the inherent factors of Industry 4.0. It presents a reference framework that generalizes the classical version to cover this gap. This paper presents a case study of the framework’s implementation. A bi-echelon optimization methodology is proposed; first, it allocates long-term recurrent tasks, and secondly it assigns daily tasks to individuals. Mathematical models are formulated to optimize workload balance and minimize idle time using the framework’s building blocks. The input datasets used were simulated based on time and frequency parameters reflecting Industry 4.0 dynamics. A total of 16 long-term and 42 short-term dataset instances were generated. The models were validated through these simulations to assess the sufficiency of the observed behavior. Results show consistent performance in adapting to variability in task types, duration, and workforce availability, presenting a reproducible approach to enhancing task allocation in cyber-physical systems.