Purpose: FMEA (Failure Mode and Effects Analysis), a widely used tool in risk management, lacks systematic guidance on selecting the type of precaution. This study introduces a rule-based FMEA. Although the Risk Priority Number (RPN), widely used in the literature, expresses the risk level quantitatively, it is insufficient for guiding the appropriate type of precaution. The proposed rule-based model uses a multi-dimensional rule system that considers numerical parameters (probability, severity, detectability, and RPN) and contextual variables. Material and Methods: The model, structured according to the Occupational Health and Safety precaution hierarchy, defines six precaution classes: elimination, substitution, engineering measures, training, administrative measures, and personal protective equipment (PPE). The model’s theoretical consistency, sensitivity, and practical applicability were tested in a neonatal intensive care unit (NICU). Results: The FMEA method identified 24 failure modes related to infections. Scenario-based sensitivity analyses revealed that contextual variables significantly influenced the recommended precautions. Administrative and training measures were the most frequently recommended, while PPE was consistently recommended for exposure-related risks. Expert evaluation indicated 95.8% agreement with the model outputs. Conclusion: These findings indicate that the development of a rule-based system can serve as a repeatable and explainable decision-support tool, especially in high-risk settings such as healthcare, which is the study’s most distinctive contribution.

Effective crew scheduling in hospitals with multiple personnel groups is essential for time efficiency and fair workload distribution. This study focuses on optimizing shift scheduling for a team of nurses, doctors, and caregivers working in the Pediatric Intensive Care Unit (PICU) of a university hospital. The model is implemented and solved using GAMS 23.5 software to minimize total staffing costs while ensuring balanced shift allocations. The scheduling process in PICUs is influenced by multiple factors, including staff skills, experience levels, personal preferences, contractual agreements, and hospital demands. Since these factors affect doctors, nurses, and caregivers differently, the model considers each personnel group separately while integrating them into a unified optimization framework. The proposed model successfully generates an annual optimal shift schedule for 10 doctors, 14 nurses, and 9 caregivers, ensuring equitable workload distribution and compliance with hospital regulations. By implementing this scheduling approach, employee satisfaction is enhanced, service quality is improved, and administrative workload is reduced. Additionally, the model ensures a well-balanced distribution of responsibilities, minimizes scheduling inefficiencies, and significantly reduces the time required for shift planning. Ultimately, this study provides a fast, fair, and cost-effective solution for hospital workforce management.

Yenidoğan yoğun bakım ünitesinde tedavi gören hasta yenidoğan bebeklerin bulundukları merkezden başka bir merkeze transportu hasta ve çalışan sağlığı açısından kritik bir öneme sahiptir. Transportun güvenli bir şekilde gerçekleşmesi ve bu sırada hastaya yoğun bakıma benzer bakım hizmetinin sağlanması gerekmektedir. Acil yardım ambulansı ve yoğun bakım üniteleri gibi iki farklı çalışma ortamının özelliklerini taşıyan yenidoğan transport ambulansları hem transport edilen yenidoğan hasta hem de transport ekibi için ilave riskler barındırmaktadır. Bu çalışmada acil yardım ambulans hizmeti, yenidoğan yoğun bakım servisi mevcut risk analizleri ve literatürde yapılmış çalışmalar dikkate alınarak yenidoğan transport ambulansları için ilk defa risk analizi yapılmıştır. Risk analizi için proaktif ve sistematik bir yaklaşım olan HTEA metodu kullanılmıştır. HTEA metoduna göre öncelikle belirlenen risk etmenlerine bağlı olarak tehlikeler belirlenmiş, bu tehlikelerin Risk Öncelik Sayısı (RÖS) hesaplanmıştır. Bu RÖS değerlerine göre önlem alınması gereken tehlikeler tespit edilerek, bu tehlikelerin kaynağında yok edilmesi ve/veya etkisinin azaltılması için önlemler önerilmiştir. Bu çalışma ile yenidoğan transport ambulanslarında çalışan ve hasta açısından kalite ve güvenliğin iyileştirilmesi ve geliştirilmesi amaçlanmıştır.

Surfactant deficiency in newborns is a result of a respiratory insufficiency condition, which is a major cause of illness and death. In terms of maintaining vital functions that require emergency intervention, it is crucial that surfactant is available for treatment upon request. The unknown times between patient arrivals and the patients stochastic weight changes in the hospital cause difficulties in determining the surfactant doses needed. The surfactant dose treatment for patients must be calculated according to the patients weight. In this study, a mathematical model that minimizes the purchase, order, holding, and waste costs of the surfactant has been developed while finding the optimum vial by considering random variables such as the time between a patients arrival and weight changes. With cost and demand affecting each other, the model uses a continuous inventory control policy, including calculating how much each preparation and vial should be used for, the reorder point, and the optimum order quantity. Also, the validity of the optimum values obtained with the mathematical model of a 66-bed neonatal intensive care unit in a hospital was tested with real data.

Logistics centers play a significant role in regional economic growth and development by optimizing logistics chains, minimizing transportation and transfer costs, shortening transit times, and enabling centralized management through support services. Intermodal transportation is an important function that enables goods to be transported efficiently using multiple modes of transport at logistics centers. This study examines 12 operational logistics centers in Türkiye, evaluating five types of transportation: unimodal (highway, railway) and intermodal (highway/railway, highway/airway, and highway/marine). The assessment considers four key criteria (transportation cost, carbon emissions, transportation risk, and transportation time) under various transportation distance and volume scenarios. The Fuzzy AHP method is employed to weight these criteria, and a goal programming model is developed to optimize transport mode selection. Among the evaluated transport modes, air transportation was not selected in any scenario due to its high cost and carbon emissions, aligning with the study’s focus on cost-efficiency and sustainability. The findings provide scenario-based recommendations for the most suitable transportation modes at each logistics center, contributing to more efficient and sustainable logistics operations.