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https://hdl.handle.net/20.500.12587/8649

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Balancing of cost-oriented U-type general resource-constrained assembly line: new constraint programming models
(Springer, 2023) Alakaş, Hacı Mehmet; Pınarbaşı, Mehmet
In simple assembly line balancing problems, it is assumed that the resources required to perform the tasks are available at the relevant station for task assignment. However, each task may need different resource types depending on the difficulty, complexity and technical requirements of the tasks in real life. For this reason, tasks and resources belonging to these tasks must be assigned to the relevant station while balancing the line. In this study, the resource-constrained U-shaped assembly line balancing problem (U-GRCALBP) is discussed. According to the literature research, there is no study dealing with U-GRCALBP. Two different constraint programming (CP) models that define the resource constraints as and/or constraint types with concurrent resource types have been developed. In these models, the sum of resource usage costs and station opening cost minimization is aimed. The models are explained with an illustrative example and the efficiency of the models is tested by deriving new resource constraints on five different data set each taking into account four different cycle times and the number of two and four resource types. The number of stations obtained, the total number of resources used, total station opening and resource usage costs, and CPU time are used as performance criteria The results are compared with the traditional U-type assembly line balancing problem results. The proposed CP models give superior performance on all data sets, especially in terms of total cost. The numerical results show that both CP models are effective in solving the problem. Furthermore, some managerial implications are presented to be useful for professionals, organizations, and society.
Assembly line balancing type-1 problem with assignment restrictions: A constraint programming modeling approach
(Pamukkale Univ, 2021) Pınarbaşı, Mehmet; Alakas, Haci Mehmet
The assembly line balancing problem (ALBP) contains some constraints which are cycle time/number of stations and precedence relations between tasks. However, due to the technological and organizational limitations, several other restrictions, such as linked tasks, incompatible tasks, station, and resource constraints, can be encountered in real production systems. In this study, we evaluate the effect of these restrictions on ALBP. For this purpose, a Constraint Programming (CP) model is proposed. The objective of the model is to minimize the number of stations for given cycle time (Type-1 problem). We investigate the solution quality of the proposed CP model according to the mixed-integer programming (MIP) and ABSALOM in terms of the several performance measurements such as the number of proofing optimal solution, number of the optimal solution, number of the best solution, relative gap between the solution with the optimal solution and average total solution time. Furthermore, the proposed approach is tested on the literature test instances, and the comparison results between models are reported. Although assignment restrictions increase the complexity of the problem, numerical experiments demonstrate that CP is an effective and high-quality solution method in solving ALBP.
Applications Of The Moora And Topsis Methods For Decision Of Electric Vehicles In Public Transportation Technology
(Vilnius Gediminas Tech Univ, 2022) Hamurcu, Mustafa; Eren, Tamer
The technological development of buses among the new alternative concepts is evaluated in this paper. Bus transportation is an important system in the public transportation, which is cheap, flexible and, in many cases, in terms of capacity and speed. But increasing car traffic in the city centre and increasing the emission such as Carbon Dioxide (CO2) in the air are some of the dangerous problems for urban life. Therefore, it is needed the public transportation to stop in-creasing car traffic and needed the cleaner technology for air and environmental quality. Electric Buses (EBs) can play an important role for resident's life quality with improving the urban air quality. However, planners and managers have dif-ficulty in decision-making due to diversified EBs together with the developing technology. Multi-criteria decision-making (MCDM) methods that are analytic decision processes, prepare a good solution for this problem. In this study, 5 EBs are assessed under the special criteria with Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Multi-Objective Optimization on the basis of the Ratio Analysis (MOORA) methods. These 2 methods are MCDM meth-ods that are used to aim of ranking of alternatives in the complex decision problem. These methods are applied to select the best EB under the 6 criteria. Finally, E5-Bus is selected as the best option that rank of the 1st at all the 3 methods. Besides, MOORA and TOPSIS methods were compared. The results are shown alongside the best bus selection for public transportation that MOORA method is also a strong tool for solving vehicle selection problems in transportation. The proposed model has been validated using existing real applications. The proposed multi-criteria analysis can be used for advising decision-makers in their decision-making process for Electric Vehicles (EVs) in the area of clean transportation.
A novel approach to optimize the maintenance strategies: a case in the hydroelectric power plant
(Polish Maintenance Soc, 2021) Özcan, Evrencan; Yumuşak, 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.
A Multi-Agent Reinforcement Learning Approach to the Dynamic Job Shop Scheduling Problem
(Mdpi, 2023) İnal, Ali Fırat; Sel, Çağrı; Aktepe, Adnan; Türker, Ahmet Kürşad; Ersöz, Süleyman
In a production environment, scheduling decides job and machine allocations and the operation sequence. In a job shop production system, the wide variety of jobs, complex routes, and real-life events becomes challenging for scheduling activities. New, unexpected events disrupt the production schedule and require dynamic scheduling updates to the production schedule on an event-based basis. To solve the dynamic scheduling problem, we propose a multi-agent system with reinforcement learning aimed at the minimization of tardiness and flow time to improve the dynamic scheduling techniques. The performance of the proposed multi-agent system is compared with the first-in-first-out, shortest processing time, and earliest due date dispatching rules in terms of the minimization of tardy jobs, mean tardiness, maximum tardiness, mean earliness, maximum earliness, mean flow time, maximum flow time, work in process, and makespan. Five scenarios are generated with different arrival intervals of the jobs to the job shop production system. The results of the experiments, performed for the 3 x 3, 5 x 5, and 10 x 10 problem sizes, show that our multi-agent system overperforms compared to the dispatching rules as the workload of the job shop increases. Under a heavy workload, the proposed multi-agent system gives the best results for five performance criteria, which are the proportion of tardy jobs, mean tardiness, maximum tardiness, mean flow time, and maximum flow time.
A Multi-Objective Perspective to Satellite Design and Reliability Optimization
(Pergamon-Elsevier Science Ltd, 2024) Tetik, Taha; Daş, Gülesin Sena; Birgören, Burak
Development of a communication satellite project is highly complicated and expensive which costs a few hundred million dollars depending on the mission in space. Once a satellite is launched into orbit, it has to operate in harsh environmental conditions including radiation, solar activity, meteorites, and extreme weather patterns. Since there is no possibility of physical maintenance intervention in space, reliability is a critical attribute for all space and satellite projects. Therefore, the redundancy philosophy and reliability measures are taken into account in the design phase of a satellite to prevent the loss of functionality in case of a failure in orbit. This study aims to optimize the payload design of a communication satellite by considering the system's reliability, power consumption and cost simultaneously. Since these objectives are conflicting in their nature, a multi-objective optimization approach is proposed. We offer a systematic approach to the satellite design by determining the best redundancy strategy considering contradictory objectives and onboard constraints in the multibillion-dollar satellite industry. The proposed approach promotes trade-offs and sensitivity analyses between cost, power consumption and system reliability in the early design phase of satellites using Compromise Programming. By using different sets of weights for the objectives in our model, it is possible to address different types of satellites depending on their mission and priorities. Because of the NP-Hard characteristics of the reliability optimization problem and the nonlinear equation in the proposed model, the Simulated Annealing algorithm is utilized to solve the problem. As a case analysis, the implementation is carried out on the design of a communication satellite system with active hot-standby and warm-standby onboard redundancy schemes. Results reveal that huge savings in million dollars can be attained as a result of approximately 5% reduction in reliability.
A model proposal for measuring service quality of eduaction with fuzzy rule-based approach and fuzzy ranking and an application
(Ios Press, 2023) Altinsoy, Ufuk; Aktepe, Adnan; Ersoz, Suleyman
In today's understanding, the universities are considered as service providers besides their institutional functions. Because the universities shape the future of the country via the services they provide, it is a necessity that their service quality must be assessed by using scientific analyses, and their service quality must be improved based on such scientific findings. The Generation Z, whose members are currently receiving university education carries unique features that distinguish them from the previous generations. When this fact is considered, it is understood that the constant research and monitoring of the learning environment of the Generation Z is important. In this study, as a result of a detailed literature search, a scale consisting of 7 dimensions and 36 indicators was developed in order to measure the higher education service quality of the Z generation. The validity and reliability tests of this scale are completed via the convergent and divergent validity analyses, Exploratory Factor Analysis (EFA), and Confirmatory Factor Analysis (CFA). Because the answers provided to the surveys reflect the personal evaluation of the participants, the Fuzzy Logic is employed, and the study is conducted by using the fuzzy modelling and fuzzy ranking. As a result of this study, the General Satisfaction Index is created, and improving recommendations are carried out based on the scores.
A Comparative Analysis of the Ranking Functions for the IVIFVs and A New Score Function
(Gazi Univ, 2022) Kokoç, Melda; Ersöz, Süleyman
The ranking of interval-valued intuitionistic fuzzy values (IVIFVs) has an important role in real-life decision-making problems. Even though there are many approaches related to the ranking methods of the IVIFVs, some of them have some shortcomings. In this study, the disadvantages of the existing ranking functions of the IVIFVs are discussed. It is revealed that both the most popular ranking functions and their recently improved versions may lead to unacceptable results. Furthermore, in this article, a new score function is offered to cope with the shortcomings of the ranking functions. The importance and performance of this new score function are proven with the help of many examples. Also, the decision-making algorithm adapted by integrating the new function is presented to illustrate the applicability of the new score function in the decision-making problems.
Maintenance prioritization of the natural gas combined cycle power plants in terms of effective portfolio management
(Gazi Univ, 2021) Alagöz, İzzet; Özcan, Nermin Avşar; Küçükyarar, Umur; Özcan, Evrencan
Revision maintenance that should be carried out at certain time intervals in power plants is of great importance in terms of increasing the availability of power plants to the highest possible level. In this context, it is a necessity to plan the revision maintenance analytically for effective portfolio management. Within this scope, although the maintenance scheduling problem of more than one power plant or unit is frequently discussed in the literature, calendar with maintenance to be performed during certain periods of the year produced by these studies is not always applicable in terms of the system dynamics and constraints of Turkey's electricity generation system. The main need is to manage the portfolios by keeping the power plants available and knowing the revision maintenance priorities that require long-term stoppages in order to both increase portfolio profitability and contribute to energy supply security. In this context, in this study, revision maintenance prioritization problem of 12 units at 3 different power plants is discussed in one of the large-scale natural gas combined cycle power plant portfolio for the first time in the literature. This study, which seeks a solution to the problem with multi-criteria decision-making approaches, is the first study in the literature to realize revision maintenance planning with long-term stoppages that affect the energy supply security at the highest level, rather than planning narrow-scoped periodic maintenance.
An artificial neural network model for maintenance planning of metro trains
(Gazi Univ, 2021) Gencer, M. Abdullah; Yumuşak, Rabia; Özcan, 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.
A Hybrid Model to Optimize the Maintenance Policies in the Hydroelectric Power Plants
(Gazi Univ, 2021) Özcan, Evrencan; Gür, Şeyda; Eren, Tamer
The main goal of power plants is to generate the electricity in sustainability perspective consisting of the principles of environmental awareness, reliability, efficiency, economy and uninterruptedness. Complying with the operational directives and maintenance are twin pillars for achieving this comprehensive goal. Within this scope, this study handles the maintenance strategy selection problem which is the first step of the effective maintenance management for one of the most important equipment groups among thousands of equipment in one of the large-scale hydroelectric power plants which have great importance for Turkey energy mix with approximately a fifth share in the total generation. So as to determine the most critical equipment group AHP-TOPSIS combination is used. For the selected equipment group, the most appropriate of all applicable 4 maintenance strategies are determined via PROMETHEE, which has been limited used for the maintenance strategy selection problem in the literature despite its advantages. As a result of this study which is the first in the literature with its method configuration and its application in hydroelectric power plants, a 1-year observation is conducted to confirm the proposed approach, and a 100% improvement is achieved in the unit shutdowns resulting from the selected equipment.
Determination of Effective Criteria for Mobile Application Selection and Sample Application
(Istanbul Univ, 2020) Uslu, Buse; Gür, Şeyda; Eren, Tamer; Özcan, Evrencan
Today everyone has specially coded and designed software for mobile phones or tablets. The 2017 data from Turkish Statistical Institute (TURKSTAT) show that there are about 78 million mobile phone users and of these the number of internet subscribers selection according to the literature review and expert opinions are language, price, performance, memory usage, user interpretation and speed. The criteria were evaluated by five officials and compared with AHP (Analytical Hierarchy Process) method to determine the significance of the criteria. Thereafter, five mobile application projects were determined and the alternatives were made by applying AHP, TOPSIS and PROMETHEE methods.
Evaluation of Profession Predictions for Today and the Future with Machine Learning Methods: Emperical Evidence From Turkey
(Gazi Univ, 2023) Karaahmetoglu, Ebru; Ersöz, Süleyman; Türker, Ahmet Kürşat; Ateş, Volkan; İnal, Ali Fırat
For the purpose of evaluating present and future trends of professions within the labor market, text mining approach could be an alternative to more traditional approaches such as employer surveys. Specifically, machine learning algorithms are used for making accurate predictions about the future directions of the professions which consequently will influence professional development of labour force. The aim of this study is to investigate the professions of the future and current in Turkey by the application of supervised learning algorithms and clustering methods to various Turkish data including documents belonging to Turkey's institutions. In this study, the popular professions were predicted with an accuracy rate between congruent to 0.81 and congruent to 0.93 thorough various machine learning algorithms. It was discovered that methodologically perceptron and stochastic gradient descent algorithms demonstrated superiority over other algorithms thanks to their intelligence functions. Furthermore, the analysis of current professions in Turkey revealed that the class of Professional occupations, Managers and Technicians and assistant professional members were popular, and according to the analysis of the future, information technology-based occupations will be important. Although limited Turkish data sources for the analysis of future, results with an accuracy of nearly 1 were produced.
Selection of Wearable Technologies for Obesity Patients with MCDM Methods
(Gazi Univ, 2022) Akıncı, Beyza Nur; Danışan, Tugba; Eren, Tamer
Obesity is our age when excessive fat accumulation in the body. It affects the whole world day by day and causes serious problems. Insufficiency of physical activity, inability to maintain energy balance, eating habits are one of the most important causes of obesity. It is used many methods in the treatment of obesity. One of these methods is wearable technologies that facilitate treatment. Wearable technology devices track daily health conditions and obtain instant data flow. These devices aim to prevent obesity by encouraging the user to move during the day and regulating their eating habits. In this study, What is the most suitable wearable technological device for obesity patients? The answer to the question was sought. For this problem solution, AHP, TOPSIS and PROMETHEE methods from Multi-Criteria Decision Making methods, were used. As a result of the solution, the Fitbit Inspire product stood out in the choice of wearable technology for obesity monitoring.
Evaluation of Wearable Health Technologies with MCDM Methods in Covid-19 Monitoring
(Gazi Univ, 2022) Deringöz, Ayşegül; Danışan, Tuğba; Eren, Tamer
Humans have struggled with many infectious diseases throughout history. Today, the coronavirus epidemic that causes the disease called Covid-19 is being fought. One of the most important factors for people with or at risk of contracting Covid-19 disease is social isolation. Many countries have developed different solutions to ensure social isolation. One of these solutions is various Wearable Health Technologies (WHT). In this study, the problem of GST selection for remote patient monitoring of Covid-19 was discussed. Six WHT products were evaluated with a total of 6 criteria, including important symptoms used in the follow-up of Covid-19 patients. Weights of 6 criteria determined by Analytical Hierarchy Process (AHP) 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 and GST products were compared. As a result of the solution, the first priority product in choosing GST for Covid-19 monitoring has been the BioButton product in both methods.
Critical Analysis on the Using of the Entropy in Multicriteria Decision Making Problems Under Interval-Valued Intuitionistic Fuzzy Environment
(Gazi Univ, 2022) Kokoç, Melda; Ersöz, Süleyman
Entropy, developed to measure information uncertainty, is taken place in the literature as an important concept after adapting to the fuzzy set theory. Fuzzy entropy gives a measure of the uncertainty of the situation, which is considered the average amount of information lost when passing from a classical pattern to the fuzzy pattern. One of the areas where entropy is used is multi-criteria decision making (MCDM) problems. In some of the studies on MCDM problems, entropy is used to calculate weights of criteria or experts, or to rank alternatives. In this study, the compatibility of entropy measures developed for the interval-valued intuitionistic fuzzy (IVIF) environment with MCDM problems is investigated. As a result of the exemplary calculations and discussions, it is seen that that the entropy functions developed for ADSB clusters does not work effectively in MCDM problems due to its theoretical properties.
A model proposal for movie theater service performance index (MTSPI) calculation with structural equation modeling and application
(Gazi Univ, 2024) Özek, Kübra İpek; Aktepe, Adnan; Ersöz, Süleyman
In this research, the objective is to create a performance index for movie theater services. In order to create the index, firstly the conceptual model for movie theater services was created. Secondly, physical evidence, social benefit, customer satisfaction and ambiance are determined as latent variables and verified with Confirmatory Factor Analysis (CFA). The relationships among latent variables are determined using the Structural Equation Model (SEM). Then Entertainment Performance Index is developed and calculated by using weights and scores of latent variables for movie theater services. Entertainment Performance Index is used for determining the level of performance and for proposing suggestions for decreasing the level of service quality gaps in movie theater services sector. In addition, satisfaction levels for different customer groups are compared according to the frequency of benefiting from revenue management applications. The ambiance dimension, which is about feeling yourself in the script and feeling the emotions more intensely in the movie theater atmosphere, was developed in this study.
Statistical and Analytical Comparison of Multi-Criteria Decision Making Methods in the Evaluation of Energy Generation Investment Alternatives: The Case of Turkey
(Gazi Univ, 2022) Özcan, Nermin Avşar; Bulut, Merve; Özcan, Evrencan; Eren, Tamer
Energy plays an important role in the countries realization of sustainable development moves in the global world. Especially, the influence level of investments to be made in the energy generation field on the development of sustainable energy policy is taken into consideration as a criterion that takes the global positions of the countries upwards. In this context, the fact that countries make investments to utilize the domestic and renewable energy resources they possess at the highest level reflects a become a necessity in terms of affecting all the criteria of economy, uninterruptedness, environmental awareness, and efficiency, which are the 4 pillars of sustainable energy policies. Accordingly, there are many studies in the literature that consider the suitability of the problem structure for multi-criteria decision-making methods and that are carried out at the regional or national level using different methods. However, in this study, unlike the literature, potential investments from all energy resources can be realized for Turkey have been evaluated under the criteria, which are calculated analytically, base of data and overarching. Energy investment alternatives ranking problem has been solved by using 7 methods/method combinations suitable for problem structure and recognized methods in the literature for the first time and obtained results have been evaluated in terms of Turkey, global energy sector and statistically. While these evaluation results have shown that wind and hydroelectric energy, which are among the renewable energy sources, contribute to the sustainable energy policy, in other hands, natural gas, which is among the fossil resources has been concluded that will be an indispensable source of investment for many years in terms of Turkey. In addition, it was concluded that the results obtained from all solution methods have a high correlation with statistical evaluation. This situation has confirmed that have been gathering significant criteria for sustainable energy. It is expected to enable creation of sustainable energy policies that can meet the ever-changing conditions by this study which includes the results that consistent with real life and statistically significant.
Determination of District Routes of Municipal Buses for YHT Station with MCDM: The Case of Kirikkale Province
(Konya Teknik Univ, 2024) Bayram, Buse; Kara, Mert; Yumuşak, Rabia; Cürebal, Ahmet; Eren, Tamer
Today, with the increasing population, the number of individual vehicles and therefore traffic density is increasing day by day. Efforts are being made to reduce both urban and intercity traffic density, and the high-speed train (HSR) project, which connects cities faster and more economically, is one of them. The fact that the HSR station will be established in K & imath;r & imath;kkale province, and more people will easily use the HSR service by making effective district connections will have a great impact on the problem of traffic density. In this context, AHP (Analytic Hierarchy Process), TOPSIS (Technique for Order Preference by Similarity to An Ideal Solution) and PROMETHEE (The Preference Ranking Organization Method for Enrichment Evaluation), which are Multi-Criteria Decision-Making (MCDM) methods, were used in an integrated manner. Based on the findings, it is observed that the second route is the preferred choice for the K & imath;r & imath;kkale University-Osmangazi line, with the optimal routes for the other lines also being determined. Since the HSR station to be opened in K & imath;r & imath;kkale will connect the districts and the university with thousands of students, the study is the most comprehensive study conducted on a provincial basis and makes an important contribution to the literature as a route determination study for HSR.
Goal Programming Model for Nurse Schedule Problem in the Pandemic Process
(Konya Teknik Univ, 2023) Koçak, Müberra; Ekren, Gizem; Yumuşak, Rabia; Eren, Tamer; Alakas, Hacı Mehmet
It is observed that nurses working in health institutions during the pandemic process, long working hours negatively affect nurses in psychological and physical terms. As a result of the pandemic process, the increasing workload in hospitals, especially the heavy and exhausting 24-hour shifts and work in the seizure system, and the fact that nurses are exposed to an irregular work schedule, the quality of work and personal life of staff is adversely affected. As a result of this, the quality of service provided to patients also decreases. In order to eliminate the negativity caused by the working conditions of nurses and to increase customer satisfaction, the problem of nurse scheduling was discussed in this study. Two solutions have been proposed for the nurse scheduling problem in the neurology intensive care unit, where 18 nurses work at the state hospital. Firstly, the solution was presented with the 0-1 goal programming method taking into account the current working conditions of the hospital. Secondly, in order to prevent the risks that may arise from a 24-hour shift, a new model was proposed by working on a two-shift system of 8 and 16 hours. In the proposed models, a fair and balanced work program has been established taking into account the rules of the hospital, the specific wishes and experiences of the nurses. This study not only addresses the real-life problem, but also provides a new perspective on the nurse scheduling problem with the analysis of the current situation and the proposal of a new shift system.

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