Ali Kokangul

A combination of deterministic and stochastic approaches to optimize bed capacity in a hospital unit

Random number of arrivals and random length of stays make the number of patients in a hospital unit behave as a stochastic process. This makes the determination of the optimum size of the bed capacity more difficult. The number of admissions per day, service level and occupancy level are key control parameters that affect the optimum size of the required bed capacity. In this study a new stochastic approximation is developed and applied to a unit of a teaching hospital. Data between 2000 and 2004 was used to obtain the necessary probability distribution functions. Mathematical relationships between the control parameters and size of the bed capacity are obtained using generated data from a constructed simulation model. Nonlinear mathematical models are then used to determine the optimum size of the required bed capacity based on target levels of the control parameters, and a profit and loss analysis is performed.

Classical and fuzzy FMEA risk analysis in a sterilization unit

Hazards of a sterilization unit of a large hospital determined.Number of FMEA classes was increased from 3 to 5.Fuzzy FMEA rules for the sterilization unit were created.Fuzzy FMEA was applied in a sterilization unit.Classic FMEA and fuzzy FMEA were compared. Global system pressures prompt hospitals to consider the risk factors of the healthcare system. The sterilization unit is a focal point of the healthcare system in regards to risk factors, and these units should be properly managed. Therefore, to study risk factors in sterilization units, we utilized failure mode and effects analysis (FMEA), which is a proactive risk assessment method for examining all failure modes and eliminating or reducing the highest risk priority failures. In this study, a 5?5 matrix and both classical and fuzzy approaches of FMEA were developed for a sterilization unit to assess and identify the hazards discussed in prior studies and new hazards discovered during this study. The method proposed in this study provided both accurate risk assessments and effective responses to those risks. Finally, a case study of the sterilization unit of a large hospital is presented to demonstrate the effectiveness of the proposed methods.

Stochastic approximations for optimal buffer capacity of many-station production lines

Probabilistic processing times, times between breakdowns and repair times make the amount of stock in buffers between stations in production lines behave as a stochastic process. Too much or too little buffer stock reduces system economy and efficiency, respectively. We obtain optimum buffer capacities and initial stock levels for production lines employing a mathematical random walk approach based on the maximum and minimum values of a stochastic process in a time window. Two approximations are developed, each useful under different risk-acceptance assumptions. Simulation results populate the equations. A motivating case study from a discrete part manufacturing line, including an example of using regression on the simulated results, is presented.

Statistical analysis of patients' characteristics in neonatal intensive care units.

The staff in the neonatal intensive care units is required to have highly specialized training and the using equipment in this unit is so expensive. The random number of arrivals, the rejections or transfers due to lack of capacity and the random length of stays, make the advance knowledge of the optimal staff; equipments and materials requirement for levels of the unit behaves as a stochastic process. In this paper, the number of arrivals, the rejections or transfers due to lack of capacity and the random length of stays in a neonatal intensive care unit of a university hospital has been statistically analyzed. The arrival patients are classified according to the levels based on the required nurse: patient ratio and gestation age. Important knowledge such as arrivals, transfers, gender and length of stays are analyzed. Finally, distribution functions for patients’ arrivals, rejections and length of stays are obtained for each level in the unit.

Optimizing nurse capacity in a teaching hospital neonatal intensive care unit

Patients in intensive care units need special attention. Therefore, nurses are one of the most important resources in a neonatal intensive care unit. These nurses are required to have highly specialized training. The random number of patient arrivals, rejections, or transfers due to lack of capacity (such as nurse, equipment, bed etc.) and the random length of stays, make advanced knowledge of the optimal nurse a requirement, for levels of the unit behave as a stochastic process. This stochastic nature creates difficulties in finding optimal nurse staffing levels. In this paper, a stochastic approximation which is based on the required nurse: patient ratio and the number of patients in a neonatal intensive care unit of a teaching hospital, has been developed. First, a meta-model was built to generate simulation results under various numbers of nurses. Then, those experimented data were used to obtain the mathematical relationship between inputs (number of nurses at each level) and performance measures (admission number, occupation rate, and satisfaction rate) using statistical regression analysis. Finally, several integer nonlinear mathematical models were proposed to find optimal nurse capacity subject to the targeted levels on multiple performance measures. The proposed approximation was applied to a Neonatal Intensive Care Unit of a large hospital and the obtained results were investigated.

A new approach for environmental risk assessment

ABSTRACT Environmental problems, such as global warming, the limited supply of sustainable energy, the depletion of natural resources, hazardous emissions released into the atmosphere and waste, are increasing global concerns. Therefore, individuals, communities, and businesses need to address environmental protection and sustainability. Environmental impact assessments are needed to identify, mitigate, and control aspects that affect the environment or a companys products, services, or activities. In this study, a general environmental aspect and impact assessment approach, which can be applied to any company that is involved in the production or service sector, is created. An environmental impact pattern that consists of 10 main and 32 sub-categories was formed based on the ISO 14001, environmental studies and field applications. The developed approach was applied to the dyeing units of a manufacturing firm. Sixteen environmental aspects were identified and assessed using the environmental impact template via the environmental failure mode and effect analysis (E-FMEA) method. The developed-approach can be applied to each sector, which will enable us to perform a detailed analysis of the environmental aspects in the environmental impact category. This approach provides a checklist for the environmental impact studies of businesses and has been pioneered as an effective method for company resources to improve their environmental performance.