ArticleHealth economics review2026
Real-time healthcare staffing optimization using fuzzy logic and genetic algorithm.
Article in Health economics review, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundThe field of medical research has advanced remarkably in the twenty-first century; however, the healthcare industry faces significant challenges, including rising costs, increasing demand, and constrained resources. These factors make effective management of healthcare systems crucial. Efficient systems are those that better use resources, deliver services, and reduce waiting times.
methodsThe paper addresses the issue of healthcare staffing optimization by presenting a new model, the Fuzzy-Based Time-Dependent Multi-Server, Multi-Queueing (FB-TDMS-MQ) System. This model combines fuzzy logic with a genetic algorithm (GA) to optimize staffing levels based on real-time patient needs and service times, a capability that traditional fixed-staffing models cannot accommodate. To demonstrate the model's application in a real-world setting, a case study is conducted at Dhanwantri Hospital and Research Centre (DHRC) in Jaipur, a multi-specialty hospital. The model's performance is evaluated using simulation studies.
resultsThe implementation of the FB-TDMS-MQ model led to significant improvements. Simulation studies demonstrated that the average "peak-hour waiting time" drastically reduced by 72.73% after incorporating fuzzy logic modifications. Additionally, the GA optimization approach resulted in a 50.2% reduction in average waiting time, showing superior performance compared to static staffing models. The model's sensitivity analysis proved highly useful in the case of unexpected events in the healthcare system.
conclusionsThe FB-TDMS-MQ model proved to be adaptable and effective in real-time healthcare staffing optimization. It demonstrated a reduction in waiting times and the ability to handle unexpected events, showing that the model could optimize staffing dynamically in fast-changing healthcare environments. The real-time control and modifications provided by the model have significant potential for improving hospital management and staff allocation.
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