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    An Artificial Neural Network Model Supported With Multi Criteria Decision Making Approaches For Maintenance Planning In Hydroelectrıc Power Plants
    (POLISH MAINTENANCE SOC, 2020) Ozcan, Evrencan; Danisan, Tugba; Yumusak, Rabia; Eren, Tamer
    Power plants are the large-scale production facilities with the main purpose of realizing uninterrupted, reliable, efficient, economic and environmentally friendly energy generation. Maintenance is one of the critical factors in achieving these comprehensive goals, which are called as sustainable energy supply. The maintenance processes carried out in order to ensure sustainable energy supply in the power plants should be managed due to the costs arising from time requirement, the use of material and labor, and the loss of generation. In this respect, it is critical that the fault dates are forecasted, and maintenance is performed without failure in power plants consisting of thousands of equipment. In this context in this study, the maintenance planning problem for equipment with high criticality level is handled in one of the large-scale hydroelectric power plants that meet the quintile of Turkey's energy demand as of the end of 2018. In the first stage, the evaluation criteria determined by the power plant experts are weighted by the Analytical Hierarchy Process (AHP), which is an accepted method in the literature, in order to determine the criticality levels of the equipment in terms of power plant at the next stage. In order to obtain the final priority ranking of the equipment in terms of power plant within the scope of these weights, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used because of its advantages compared to other outranking algorithms. As a result of this solution, for the 14 main equipment groups with the highest criticality level determined on the basis of the power plant, periods between two breakdowns are estimated, and maintenance planning is performed based on these periods. In the estimation phase, an artificial neural network (ANN) model has been established by using 11-years fault data for selected equipment groups and the probable fault dates are estimated by considering a production facility as a system without considering the sector for the first time in the literature. With the plan including the maintenance activities that will be carried out before the determined breakdown dates, increasing the generation efficiency, extending the economic life of the power plant, minimizing the generation costs, maximizing the plant availability rate and maximizing profit are aimed. The maintenance plan is implemented for 2 years in the power plant and the unit shutdowns resulting from the selected equipment groups are not met and the mentioned goals are reached.
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    Öğe
    Comparison and selection of patient follow-up systems for covid-19 pandemic patients
    (Springer, 2022) Eren, Tamer; Danisan, Tugba; Deringoz, Aysegul; Aksut, Guler
    People have struggled with many infectious diseases throughout history. Today, the Covid-19 is being fought. One of the most important things for people who have or are at risk of getting Covid-19 is social isolation. Many countries resort to different ways to ensure social isolation. For this, remote patient monitoring systems have been developed. In this study, the problem of the selection of Covid-19 remote patient monitoring systems is discussed. Seven Wearable Health Technology (WHT) products were evaluated with a total of 10 criteria, including the important symptoms used in the patient tracking systems. The weights of 10 criteria determined by the Analytical Hierarchy Process (AHP) method were calculated, and these weights were used in the solution of The Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE), and Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS) methods. WHT products were compared. As a result, the most appropriate patient follow-up system was determined. This study generates differences in terms of evaluating seven different products and ten criteria in total with MCDM methods. A more comprehensive evaluation has been made in the literature than the studies in this field.
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    Goal Programming Approach For The Radiology Technician Scheduling Problem
    (Yildiz Technical Univ, 2019) Ozcan, Evrencan; Danisan, Tugba; Yumusak, Rabia; Gur, Seyda; Eren, Tamer
    Population growth is led to an increase in demand in the health sector. Health services need to be met at the maximum level in the face of increasing demand. The number of patients per each health personnel in Turkey is too much. Considering this situation, it is seen that ensuring patient satisfaction is directly proportional to the satisfaction of the personnel. For this reason, some studies such as the positioning of polyclinics and hospitals encountered in health services, capacity planning and demand estimations, as well as the studies about the creation of study schedules of health personnel are also gained importance. In this study, it is aimed to provide the personnel satisfaction as much as possible. The Law No. 3153 on Radiology, Radionomy and Electrical Therapy and Other Physiotherapy Institutions published by the Ministry of Health of the Republic of Turkey, dated 19/4/1937, as well as the requirements of the Annex-1 of 21/1/2010, is considered. The scheduling problem for eight radiology technicians working in a private hospital in Ankara is discussed. A mathematical model is proposed using the goal programming method in order to assign the technicians to the four shifts as equally as possible. According to the researches, this is the first study which has the feature of radiology technicians and by considering government and hospital conditions as well as with staff requests, by application area.