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Proceedings of the 21st Conference on Computer Science and Intelligence Systems (FedCSIS)

Annals of Computer Science and Information Systems, Volume 47

Stochastic Simulation and Optimization of Human Resource Management Processes in Higher Education

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DOI: http://dx.doi.org/10.15439/2026F7729

Citation: Małgorzata Oleś-Filiks,

Full text

Abstract. The administration of human resources in Higher Education Institutions (HEIs) involves complex, multi-departmental workflows that are highly susceptible to fluctuations in demand. This paper presents a discrete-event simulation study of three critical academic HR processes: Employment, Ongoing HR Services, and Dismissal of academic teachers. Utilizing the Prosimos stochastic simula- tion engine via the AureaSim wrapper, we evaluated the current Business Process Model and Notation (BPMN) models against four operational scenarios: Baseline, Peak Load, Recovery Plan, and Cost Optimization. Empirical simulation data (n=500 cases per process) reveals that baseline configu- rations are highly vulnerable to peak loads, with the cycle time for employment degrading from 5.08 days to over 385 days. Through hypothesis testing, we identified structural inefficiencies such as ``scale bottlenecks'' in routine tasks and ``cost bottlenecks'' in legal compliance. Strategic resource augmentation and targeted optimizations---such as robotic process automation (RPA) and delegating executive approvals---demonstrate that sub-day cy- cle times can be restored while managing operational costs. The insights gained offer actionable data for strategic decision-making regarding resource management and digital transformation in academic environments.

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