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    A novel approach to optimize the maintenance strategies: a case in the hydroelectric power plant
    (Polish Maintenance Soc, 2021) Ozcan, Evrencan; Yumusak, Rabia; Eren, Tamer
    Countries need to develop sustainable energy policies based on the principles of environmental sensitivity, reliability, efficiency, economy and uninterrupted service and to maintain their energy supply in order to increase their global competitiveness. In addition to this impact of sustainable energy supply on the global world, maintenance processes in power plants require high costs due to allocated time, materials and labor, and generation loss. Thus, the maintenance needs to be managed within a system. This makes analytical and feasible maintenance planning a necessity in power plants. In this context, this study focuses on maintenance strategy optimization which is the first phase of maintenance planning for one of the large-scale hydroelectric power plants with a direct effect on Turkey's energy supply security with its one fifth share in total generation. In this study, a new model is proposed for the maintenance strategy optimization problem considering the multi-objective and multi-criteria structure of hydroelectric power plants with hundreds of complex equipment and the direct effect of these equipment on uninterrupted and cost-effective electricity generation. In the model, two multi-criteria decision-making methods, AHP and COPRAS methods, are integrated with integer programming method and optimal maintenance strategies are obtained for 571 equipment.
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    An artificial neural network model for maintenance planning of metro trains
    (Gazi Univ, 2021) Gencer, M. Abdullah; Yumusak, Rabia; Ozcan, Evrencan; Eren, Tamer
    In urban transportation, trains have an increasingly important place due to the increase in the number of passengers. Meeting the number of passengers is directly related to the number of trains operated on a line. Thus, the frequency of operation of trains affects the level of wear of the equipment. This makes train maintenance more important. Equipment faults are the basis for train maintenance. However, the fault times of the equipment which are unknown causes uncertainty in the maintenance activities and plans. This uncertainty results from many factors that affect the faults of the train. If historical maintenance data, fault data, and factors affecting the faults are known, effective use of resources (time, cost and personnel, etc.) is provided and uncertainty is eliminated. In this study, firstly, maintenance data in Ankara Metro between 2017 and 2018 is examined and the factors affecting equipment faults are evaluated with expert opinion. Artificial Neural Network (ANN) model is created with the data set and this data set along with the factors affecting each the equipment fault according to the type of equipment. In the ANN model, 5 factors (Equipment Type, Preventive Maintenance Frequency, Material Quality, Life Cycle, Line Status) affecting the faults of the equipment is determined as inputs and the number of failures as outputs. The mean absolute percent error (MAPE) value is found as 11%, and the mean square error value (MSE) is 0.0028229 in the training and test stages of ANN. Then, the frequency of fault is found according to the equipment fault and a 10-week maintenance planning is applied. The results are compared with current maintenance planning. As a result of the applied maintenance planning, the average number of faults of the trains decreases by 27%, uninterrupted service rate increases by 40% and heavy maintenance errors are also prevented. Fault removal times resulted in a 10% improvement. The results showed that ANN models could be used effectively in fault prediction and maintenance planning with rail system multiple types of equipment. In the literature, there is no study that implements maintenance planning with an ANN model where all train equipments and factors affecting the failure are evaluated together. This study is the first in the field of rail systems maintenance in the literature and will be a reference for future studies.
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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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    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.
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    Risk Based Maintenance in the Hydroelectric Power Plants
    (Mdpi, 2019) Ozcan, Evrencan; Yumusak, Rabia; Eren, Tamer
    In this study, maintenance planning problem is handled in one of the hydroelectric power plants which directly affect Turkey's energy supply security with a fifth share in the total generation. In this study, a result is obtained by taking into consideration the multi-objective and multi-criteria structure of the maintenance planning in the hydroelectric power plants with thousands of complex equipment and the direct effect of this equipment on uninterrupted and low-cost electricity generation. In the first stage, the risk levels of the equipment in terms of the power plant are obtained with the combination of AHP (Analytical Hierarchy Process) and TOPSIS (technique for order preference by similarity to ideal solution) which are frequently used in the literature due to their advantages. Department-based maintenance plans of all equipment for periodic and revision maintenance strategies are formed by integrating these values into the time allocated for maintenance and the number of employees constraints. As a result of the application of this methodology which is designed for the first time in the literature with the integration of multi-criteria decision-making methods for the maintenance planning problem in a hydroelectric power plant, all elements that prevent the sustainable energy supply in the power plant are eliminated.
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    Selection of Medicine Warehouse and Vaccine Distribution Center for Sustainable Supply Chain Management in the Pandemic Process
    (Gazi Univ, 2022) Oral, Nursena; Yapici, Selma; Yumusak, Rabia; Eren, Tamer
    Demand for the medical equipment and pharmaceutical industry is gradually increasing with the effect of health problems that have diversified in the process from past to present. In addition to this demand, the spread of the COVID-19 virus, which has affected the whole world, has made it necessary to easily access medical supplies. With the results of the vaccine studies conducted in this period, which competes with time, the supply of the resource to the consumers emerges as a critical problem. For this reason, the problem of choosing the place for the medicine warehouse and vaccine distribution center, which includes health products, was discussed in the study. For the warehouse organization that will serve the pharmaceutical and vaccine industry, it is aimed to select the most suitable warehouse location by using multi criteria decision making methods (MCDM), AHP (Analytic Hierarchy Process), ANP (Analytic Network Process) and PROMETHEE methods. In the problem under consideration, 5 alternative scenario solutions have been compared with each other. 4 alternative regions in Kirikkale province were evaluated in terms of its strategic location, and solutions were realized by taking 3 main criteria and 7 sub criteria into consideration. With this study, solution comparison of AHP, ANP and PROMETHEE methods was made for the first time for the problem of choosing medicine warehouse and vaccine distribution center location and contributes to the literature.