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A Comparative Analysis of the Ranking Functions for the IVIFVs and A New Score Function
(Gazi Univ, 2022) Kokoc, Melda; Ersoz, Suleyman
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.
A comparison of the performance of entropy measures for interval- valued intuitionistic fuzzy sets
(Yildiz Technical Univ, 2021) Kokoc, Melda; Ersoz, Suleyman
Entropy measure is a significant tool to define unclear information. But, entropy measures for interval-valued intuitionistic fuzzy sets (IVIFSs) cannot be easily understood intuitively. So, it is highly important to compare the existing measures to select a reliable entropy measure in studies. The purpose of this study is to compare the performance of different entropy measures developed for IVIFSs. The numerical examples are presented to show whether entropy measures for IVIFSs are effective in representing the fuzziness degree. In order to understand whether a variation of fuzziness degree of one or more elements of IVIFSs change the ranking results, selected IVIFSs are modified diversely.
A Fuzzy Analytic Hierarchy Process Model For Supplier Selection And A Case Study
(Kırıkkale Üniversitesi, 2011) Aktepe, Adnan; Ersoz, Suleyman
In today’s competitive manufacturing and service industries decision making is a critical process. Supply chain management is a network of businesses and in this network there are several critical decision making problems. One of them is supplier selection decision. Supplier selection is a multi-criteria decision making problem and a fuzzy decision making model is proposed to this problem area in supply chain management. The extent analysis method and integral value calculation is used in the study for computing the priority weights of criteria and alternatives. In addition, a case study is added to the study.
A LITERATURE REVIEW OF INTERVAL-VALUED INTUITIONISTIC FUZZY MULTI-CRITERIA DECISION-MAKING METHODOLOGIES
(Wroclaw Univ Science & Technology, Fac Management, 2021) Kokoc, Melda; Ersoz, Suleyman
Multi-criteria decision-making (MCDM) is one of the most popular problems handled by researchers in the literature. Since the interval-valued intuitionistic fuzzy set (IVIFS) theory generates as realistic as possible evaluation of linguistic expressions, researchers have been expanding traditional MCDM methods to the IVIF environment, especially in the last decade. This study provides a literature review of the relevant articles from several academic databases on applications of IVIF-MCDM methods. The review of 131 publications addresses specific research questions. To understand the research publication trend, this review offers a visual analysis that examines the studies from different perspectives, such as application areas, IVIF-MCDM methods, citations, most relevant journals, and validation methods. One of the most remarkable results of the literature review is that most publications in this field are published in SCIE indexed journals. Another noteworthy issue is that China is the country that produces the most articles in this field. In addition, English journals are mostly selected for the publication of articles. While it is seen that the investment selection problem is chosen mostly as the application area, the TOPSIS method is preferred mostly in the applications. This study stands out as the most comprehensive one that compiles publications containing extended traditional MCDM methods for IVIF sets. This review will be an important reference for future researchers and decision-makers involved in advancing MCDM methods considering vagueness and ambiguity.
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 Multi-Agent Reinforcement Learning Approach to the Dynamic Job Shop Scheduling Problem
(Mdpi, 2023) Inal, Ali Firat; Sel, Cagri; Aktepe, Adnan; Turker, Ahmet Kursad; Ersoz, Suleyman
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 Systematic Literature Review of the Predictive Maintenance from Transportation Systems Aspect
(Mdpi, 2022) Ersoz, Olcay Ozge; Inal, Ali Firat; Aktepe, Adnan; Turker, Ahmet Kursad; Ersoz, Suleyman
With the rapid progress of network technologies and sensors, monitoring the sensor data such as pressure, temperature, current, vibration and other electrical, mechanical and chemical variables has become much more significant. With the arrival of Big Data and artificial intelligence (AI), sophisticated solutions can be developed to prevent failures and predict the equipment's remaining useful life (RUL). These techniques allow for taking maintenance actions with haste and precision. Accordingly, this study provides a systematic literature review (SLR) of the predictive maintenance (PdM) techniques in transportation systems. The main focus of this study is the literature covering PdM in the motor vehicles' industry in the last 5 years. A total of 52 studies were included in the SLR and examined in detail within the scope of our research questions. We provided a summary on statistical, stochastic and AI approaches for PdM applications and their goals, methods, findings, challenges and opportunities. In addition, this study encourages future research by indicating the areas that have not yet been studied in the PdM literature.
An Expert System Approach For High School Type Selection
(Gazi Univ, 2011) Ersoz, Suleyman; Aktepe, Adnan
An expert system is a computer program that imitates the procedures by which experts solve problems. This paper describes an expert system application which provides advice to primary school students who are seeking assistance in determining high school type. The system gathers information about student grade point average (GPA), centralized exam score, personality type and socio-economic factors. It assesses student qualifications for seven different school types in Turkey. The expert system recommends a school type for the students and produces a short report explaining the reasons of recommendation. Student GPA and centralized exam score are considered as technical criteria. In addition, social and socio-economic factors such as personality type, parental involvement etc.affecting the decision process are also considered and included in the model.
Backpropagation Neural Network Applications for a Welding Process Control Problem
(Springer-Verlag Berlin, 2012) Aktepe, Adnan; Ersoz, Suleyman; Luy, Murat
The aim of this study is to develop predictive Artificial Neural Network (ANN) models for welding process control of a strategic product (155 mm. artillery ammunition) in armed forces' inventories. The critical process about the production of product is the welding process. In this process, a rotating band is welded to the body of ammunition. This is a multi-input, multi-output process. In order to tackle problems in the welding process 2 different ANN models have been developed in this study. Model 1 is a Backpropagation Neural Network (BPNN) application used for classification of defective and defect-free products. Model 2 is a reverse BPNN application used for predicting input parameters given output values. In addition, with the help of models developed mean values of best values of some input parameters are found for a defect-free weld operation.
Creating Alternative Layout Plans with Simulated Annealing and Data Mining
(Ieee, 2017) Kokoc, Melda; Aktepe, Adnan; Ersoz, Suleyman
When studies in literature are examined, it is seen that different approaches have been used to solve facility layout problems. The relationship between departments in layout is always important. In this study, data mining technique is used for analyzing relations among departments and then association rules are obtained. Determining closeness relationships between the departments in facility are often ambiguous and require expert opinions. In such cases, a fuzzy component emerges in the facility layout problem. Hence, fuzzy logic is widely used to address ambiguous problems. Association rules is converted by using defuzzification approach to crisp values used facility layout problem solution in this study. Facility layout problems are considered to be NP-Hard (Nondeterministic-Polynomial-Hard) optimization problems. That is, definite solution approaches are limited in solving large-scale problem examples. For heuristic approaches are frequently used to improve the layout, simulated annealing approach is used in this study. To improve facility layout planning, simulated annealing approach is carried out via code written in Visual Basic 2012. In conclusion, 17% improvement is achieved with alternative layout plan obtained.
Customer satisfaction and loyalty analysis with classification algorithms and Structural Equation Modeling
(Pergamon-Elsevier Science Ltd, 2015) Aktepe, Adnan; Ersoz, Suleyman; Toklu, Bilal
Businesses can maintain their effectiveness as long as they have satisfied and loyal customers. Customer relationship management provides significant advantages for companies especially in gaining competitiveness. In order to reach these objectives primarily companies need to identify and analyze their customers. In this respect, effective communication and commitment to customers and changing market conditions is of great importance to increase the level of satisfaction and loyalty. To evaluate this situation, level of customer satisfaction and loyalty should be measured correctly with a comprehensive approach. In this study, customers are investigated in 4 main groups according to their level of satisfaction and loyalty with a criteria and group based analysis with a new method. We use classification algorithms in WEKA programming software and Structural Equation Modeling (SEM) with LISREL tools together to analyze the effect of each satisfaction and loyalty criteria in a satisfaction-loyalty matrix and extend the customer satisfaction and loyalty post-analysis research bridging the gap in this field of research. To convert developed conceptual thought to experimental study, white goods industry is exemplified. 15 criteria are used for evaluation in 4 customer groups and a satisfaction-loyalty survey developed by experts is applied to 200 customers with face-to-face interviews. As a result of the study, a customer and criteria grouping method is created with high performance classification methods and good fit structural models. In addition, results are evaluated for developing a customer strategy improvement tool considering method outcomes. (C) 2014 Elsevier Ltd. All rights reserved.
Demand forecasting application with regression and artificial intelligence methods in a construction machinery company
(Springer, 2021) Aktepe, Adnan; Yanik, Emre; Ersoz, Suleyman
Demand forecasts are used as input to planning activities and play an important role in the management of fundamental operations. Accurate demand forecasting is an important information for many organizations. It provides information for each stage of inventory management. In this study, multiple linear regression analysis, multiple nonlinear regression analysis, artificial neural networks and support vector regression were applied in a production facility that produces spare parts of construction machinery. The aim of the study is to forecast the number of spare parts requested in the future period by the customer as close as possible. As the input variables in the developed models, the sales amounts of the past years belonging to the manifold product group, which is one of the important spare parts of the construction machinery, number of construction machines sold in the world, USD exchange rate and monthly impact rate are used as input variables. The inputs of the model are designed according to construction machinery sector. In the model, monthly impact rate enables us to create more robust model. In addition, the estimation results have high accuracy by systematic parameter design of artificial intelligence methods. The data of the 9 years (from 2010 to 2018) were used in the application. Demand forecasts were conducted for 2018 to compare actual values. In forecasts, artificial neural network and support vector regression produced better results than regression methods. In addition, it was found that support vector regression forecasting produced better results in comparison to artificial neural network. __________________________________________________________________________________________
Demand forecasting of spare parts with regression and machine learning methods: Application in a bus fleet
(Academic Publication Council, 2023) Ifraz, Metin; Aktepe, Adnan; Ersoz, Suleyman; cetinyokus, Tahsin
Forecasting the demand of spare parts of vehicles in bus fleets is a vital issue. Vehicles must operate effectively and must have a high availability rate in the fleet. In maintenance operations, faulty parts or parts that complete their lifetime must be replaced with a new one. Spare parts needed must be in inventories with the required amount on time. In this sector, there are thousands of spare parts to manage. The maintenance and repair department must operate effectively. In order to accomplish this, accurate forecast of spare parts is required. In this study, demand forecasting was carried out with regression-based methods (multivariate linear regression, multivariate nonlinear regression, Gaussian process regression, additive regression, regression by discretion, support vector regression), rule-based methods (decision table, M5Rule), tree-based methods (random forest, M5P, Random tree, REPTree) and artificial neural networks. The forecasting model developed in this study includes critical variables such as the number of vehicles in the fleet, the number of breakdowns that cause parts to change, the number of periodic maintenance, mean time between failure and demand quantity in previous years. The application was carried out with real data of eight (2013-2020) years. 2013-2019 data was used for training and 2020 data was used for testing. In forecasts, support vector regression among regression-based methods, decision table among rule-based methods, M5P among tree-based methods gave the best results. It has been observed that the artificial neural network produced more accurate forecasts than all other methods. Artificial neural network forecasts give the highest forecast accuracy rate and the least deviation.
Estimation algorithms for Weibull parameters and percentiles
(Gazi Univ, Fac Engineering Architecture, 2009) Danaci, Mehmet Akif; Birgoren, Burak; Ersoz, Suleyman
This study concerns the use of Weibull distribution in statistical component reliability. Recently, estimation of confidence intervals and confidence lower bounds for Weibull parameters and percentiles in small samples has received increasing attention in the literature. In expensive or long experiments, it is crucial to keep the sample size to a minimum, however, the estimates become less reliable as the sample size decreases. Therefore, it has become a necessity to perform a comparative study of estimation algorithms for small sample sizes and code them in an efficient manner. In this study, uncensored reliability data have been considered; algorithms have been developed for goodness-of-fit tests, point and confidence interval estimation for parameters and percentiles by the maximum likelihood and weighted least squares methods based on simulation. The algorithms have been generated in the standard C++ language and integrated under a computer interface. Similar studies in the literature were performed only for a limited number of failure probabilities, confidence levels and sample sizes with low simulation run numbers; the user has to use coefficients and formulae obtained from the simulations to produce the estimates. In contrast, the algorithms developed in this study allow the user to perform simulations with any run number, failure probability, confidence level and sample size, and calculate the estimates in a reasonable amount of time. The simulation error can be kept at low levels by specifying large simulation run numbers. Also, the application of the interface has been illustrated on failure times of DC motors.
Examining the Effect of Revenue Management on Customer Perceptions and Calculating the Service Performance Index: Food Order Application Example
(Mdpi, 2022) Ozek, Kubra Ipek; Ersoz, Suleyman; Aktepe, Adnan; Teslim, Sila
In this research, the target is to create a Service Performance Index for food order mobile applications. In order to create the index, a structural equation model is developed. Then the coefficients which are obtained from the model are used to calculate the index values. There are some revenue management practices carried out in the food sector such as discounts when you order a meal for two or privileges for contracted credit card or mobile line users. In this context, this study tried to measure whether there is a relationship between the revenue management and customer perceptions, which are e-service quality, satisfaction and word of mouth. It was realized that revenue management has a significant, positive and high-level effect on e-service quality, satisfaction and word of mouth. The index scores of participants of the questionnaire were compared according to the frequency of benefiting from revenue management applications and discount campaigns. Thus, it was revealed that the service performance index of those who always use revenue management applications is higher than those who never use them. This result contributes to businesses with an important reference in terms of food marketing strategy.
Internet Based Intelligent Hospital Appointment System
(Taylor & Francis Inc, 2015) Aktepe, Adnan; Turker, A. Kursad; Ersoz, Suleyman
In today's competitive service industry, the technology in service systems is used in a wide range of areas. The service companies are now providing service via internet or via other computer based systems in an increasing trend day by day. Expert systems are good examples of these applications. Today, expert systems are used in various fields such as design, planning, imaging, diagnosis, etc. For practical use, the expert systems are also used through internet. One of the most important service system institutions is the hospital. Increasing the service quality level in hospitals, internet based appointment systems are used in several hospitals in Turkey. There are also several internet based expert system applications today contributing the improvement of service levels in several industries. In this study, an internet based expert system is created that is used in outpatient department/polyclinic direction. The system architecture, algorithm and the role of such an expert system are discussed in this paper. With the help of internet based expert appointment system at hospitals, the queues in the hospitals decline, the number of consultations decreases, the patients and doctors save time and finally the customer satisfaction and quality level increases.
Investigation of Food Supply Chain Sustainability Performance for Turkey's Food Sector
(FRONTIERS MEDIA SA, 2020) Yontar, Emel; Ersoz, Suleyman
Increasing product demands, environmental aspects, and overpopulation have an impact on the sustainability of a supply chain, especially in the food sector. In a food supply chain from production to consumption, there are many parameters (resources, packaging, waste management, etc.) that need to be taken into account for sustainability. The aim of this study is to determine the parameters affecting sustainable food supply chain management for the food sector and to measure the performance of the parameters along the supply chain. In this study, several performance indices and sub-criteria are defined by reviewing the indices previously discussed in the literature and accounting for expert opinions on the sustainable food supply chain. Customer satisfaction, resource utilization, product safety, innovation, reliability, company information, packaging, and waste management are defined as the parameters, as these are dimensions that should be improved in sustainable food supply chain management. The performance score for each dimension is calculated via Structural Equation Modeling and the Analytic Hierarchy Process. Customer satisfaction is calculated to have the highest performance, with a score of 86.23% in sustainable food supply chain management performance evaluation, followed by the product safety dimension, at 84.65%, while the performance index score of the reliability dimension is 82.97%, that of the packaging dimension is 78.81%, that of the company information dimension is 75.10%, that of resource utilization is 71.41%, and that of the waste management dimension is 67.83%. The sustainable food supply chain performance evaluation for the food sector in Turkey indicates that it has an overall performance of 79.7%. The results of this study include feedbacks on parameters in the food chain from agriculture to consumers.
A multi-stage satisfaction index estimation model integrating structural equation modeling and mathematical programming
(Springer, 2019) Aktepe, Adnan; Ersoz, Suleyman; Toklu, Bilal
In this study, a satisfaction index estimation model is proposed integrating structural equation modeling and mathematical programming methods with fuzzy customer data. Firstly, a deep literature survey is conducted in this field of study. Then, a new model is proposed by taking into consideration gaps in the literature. The estimation model is composed of five stages and first stage is building conceptual model in which measurement and latent variables are introduced. At the second stage, a fuzzy evaluation method is developed for decreasing subjectivity in customer data. At the third stage, for measurement variables that are directly observed, a measurement model is developed with Linear Structural Relations. In the solution of the measurement model maximum likelihood algorithm is used. In the solution of structural model that is composed of latent variables that are not directly observed, a mathematical estimation model is developed in this study at the fourth stage. Mathematical model is coded in ILOG Cplex Optimization Studio. In the mathematical model that minimizes estimation errors, structural relations and measurement variable weights (precedence coefficients) are defined as constraints. At the fifth and last stage, index scores are calculated with mathematical model outputs. Application of the model is carried out in public sector at a local government service point. In the application model, service quality, innovation, communication, satisfaction and cost perception dimensions are used. Application results are discussed for both measurement and latent variables in detail. The results of model we developed are also compared with an alternative model outcomes and we show that we achieve optimum estimation capability with minimum estimation errors.
New Ranking Functions for Interval-Valued Intuitionistic Fuzzy Sets and Their Application to Multi-Criteria Decision-Making Problem
(Inst Information & Communication Technologies-Bulgarian Acad Sciences, 2021) Kokoc, Melda; Ersoz, Suleyman
Many authors agree that the Interval-Valued Intuitionistic Fuzzy Set (IVIFS) theory generates as realistic as possible evaluation of real-life problems. One of the real-life problems where IVIFSs are often preferred is the Multi-Criteria Decision Making(MCDM) problem. For this problem, the ranking of values obtained by fuzzing the opinions corresponding to alternatives is an important step, as a failure in ranking may lead to the selection of the wrong alternative. Therefore, the method used for ranking must have high performance. In this article, a new score function S-KE and a new accuracy function H-KE are developed to overcome the disadvantages of existing ranking functions for IVIFSs. Then, two illustrative examples of MCDM problems are presented to show the application of the proposed functions and to evaluate their effectiveness. Results show that the functions proposed have high performance and they are the eligibility for the MCDM problem.
New Score and Accuracy Function for IVIF Sets and Their Applications to AHP for MCGDM
(Taylor & Francis Inc, 2022) Kokoc, Melda; Ersoz, Suleyman
Multi-criteria group decision making (MCGDM) is an important part of decision science and widely applied in several fields. To cope with the vagueness and ambiguity of the human evaluation in the MCGDM process, interval-valued intuitionistic fuzzy set (IVIFS) theory is introduced. In this study, the AHP method whose traditional form is frequently used in MCGDM problems is improved in the IVIF environment. Since the aim of AHP is to choose the most suitable criterion/alternative by making a rank from the most important to the least important, the improved IVIF-AHP method is based on the ranking functions. Some existing score and accuracy functions are analyzed and it is seen that these functions have some limitations to rank IVIF numbers. So, new score and accuracy functions of IVIFS are developed to overcome their limitations and these new functions are integrated into the IVIF-AHP method. The comparative results show that the improved IVIF-AHP method gives a consistent ranking for the criteria. Moreover, the proposed method steps are illustrated with a case study about prioritizing some technical and soft skills over industrial engineering and electrical engineering.

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