Endüstri Mühendisliği Bölümü Makale Koleksiyonu

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https://hdl.handle.net/20.500.12713/173

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Kullanılmayan ve atık ilaçların tersine lojistik faaliyetleri ile toplanmasına tüketicinin bakış açısının değerlendirilmesi
(Marmara Üniversitesi, 2022) Karadayı Usta, Saliha
İlaç endüstrisi, atık ilaçların çevre ve insan sağlığına verdiği zararların günümüzde daha sık ve net şekilde vurgulanması ile atık yönetimi konusuna gerekli önemin verilmesi konusunda çeşitli uygulamalar ve yönetmelikler sunmaktadır. Bu kapsamda kullanılmayan ve atık ilaçların uygun şekilde ve uygun zaman diliminde toplanması, bilinçli tüketicilerin bu konuda gerekli iş birliği sergilemesi önem arz etmektedir. Dolayısıyla bu araştırmanın amacı, kullanılmayan ve atık ilaçların tersine lojistik faaliyetleri ile toplanmasına tüketicinin bakış açısını anlamak, değerlendirmek ve çözüm önerileri geliştirmektir. Bu amaçla anket yoluyla veri toplanmakta, tanımlayıcı araştırma modeli kullanılmakta ve durum değerlendirmesi yapılmaktadır. Anketin ilk kısmında Aktaş ve Selvi (2019) tarafından geliştirilen Akılcı İlaç Kullanımı Farkındalığı ölçeğine başvurulmakta, takiben bu araştırmaya özgü merak edilen sorular yöneltilmektedir. Bulgular incelendiğinde tüketicinin ilaç bağışlama konusunda olumlu tavır sergilediği, ancak kullanılmayan ilacı kabul edip tüketmeye olumsuz baktığı anlaşılmaktadır. Arada kontrolü sağlayacak bir otoritenin bulunması ile güven ortamının oluşacağı düşünülmektedir. Ayrıca, ilaçların son kullanma tarihlerinin hatırlatılması için uyarı sistemlerine / hatırlatıcılara olumlu bakılmakta, özellikle telefona mesajla hatırlatma gelmesini talep etmektedir. Otomat / akıllı kutu gibi sistemi kolaylaştırıcı araçların yaratabileceği risklere önemle vurgu yapılmaktadır. Toplumun konu hakkında bilgilendirilmesi, eğitim yoluyla küçük yaşlarda farkındalık yaratılması ihtiyacının altı çizilmektedir. Çalışma çıktıları, ilaç atıklarının geri toplanmasında tüketici davranışının anlaşılmasını, uygulama aşamasına geçildiğinde ise ne tip önerileri / çözüm yolları izlemenin nasıl sonuçlar doğuracağını tahmin etme hususunda katkı sağlamaktadır. Konuyla ilgili ilaç endüstrisi yetkililerine yol gösterici çıktılar sunulmaktadır.
Sürdürülebilir moda tedarik zinciri yönetimi uygulamalarının sistematik taraması
(LODER, 2022) Karadayı Usta, Saliha
Dijitalleşmenin ve e-ticaret uygulamalarının geniş kitlelerce benimsenmesi, sosyal medyada görünür ve beğenilir olma kaygısı gibi sebepler, moda endüstrisinde neredeyse her hafta yeni koleksiyon tanıtmaya kadar giden bir hızlı üretim ve tüketim çılgınlığına sebep olmuştur. Hızlı moda akımının getirdiği en endişe verici sonuçlar arasında; kullanılan hammaddede sadece ucuzluğun dikkate alınarak sentetik kumaş kullanımının bilinçsizce artması, yine sadece ürün fiyatının düşük tutulması amacı ile tedarikçilere ödemelerin zamanında ve olması gereken miktarda yapılmaması, mevcut yerel tedarikçilerin sunduğu hizmet seviyesine ve kaliteye bakmaksızın daha ucuz mamul peşinde yurt dışı kaynaklara yönelim bulunmaktadır. Ayrıca iklim krizinin görünür hale gelmesiyle, doğal kaynakların minimum seviyede kullanımını temel alan sürdürülebilir moda hareketi büyük önem taşımaktadır. Sürdürülebilir moda kapsamında gerçekten ihtiyaç hissedildiği durumda alışveriş yapılmalı, daha dayanıklı ve kaliteli giysiler tasarlanmalı, giysiler onarılarak ürün ömrü uzatılmalı, ikinci el giyim alışverişi desteklenmeli, kullanım ömrü sona erdiğinde giysiler ipliğe veya kumaşa geri dönüştürülmeli, etik çalışma koşulları altında sürdürülebilir bir ağ kurulmalı ve bu ağ dijital kanallar üzerinden izlenmelidir. Dolayısıyla bu çalışmanın amacı, sürdürülebilir moda/tekstil/giyim tedarik zinciri literatüründe yer alan anahtar kavramları bibliyometrik analiz yoluyla belirlemek, bu kavramlar arasındaki ilişkileri ağ diyagramları ile görselleştirmek ve kavramların detaylarını vakalarla desteklemektir. Çalışmanın bulguları doğal kumaş üreticilerini, etik çalışma koşullarını sağlayan tedarikçi seçimini, tüm tedarik zinciri boyunca izlenebilirliğin sağlanmasını, yeşil lojistik uygulamalarını, bilinçli tüketici tutumlarını, ürünlerin döngüde tutularak uzun süreli kullanılmasını ve ömrünü tamamlamış ürünlerin geri dönüştürülmesini önemle vurgulamaktadır.
Technical challenges of blockchain technology for sustainable manufacturing paradigm in Industry 4.0 era using a fuzzy decision support system
(Elsevier Ltd, 2023) Su, Dan; Zhang, Lijun; Peng, Hua; Saeidi, Parvaneh; Tirkolaee, Erfan Babaee
Since 2008, many academics have increasingly paid attention to blockchain technology from different perspectives. In general, researchers desire to achieve global blockchain systems within a sustainable manufacturing domain; however, a number of technical challenges have come to exist in the recent decade, for instance, consensus algorithms and computing paradigms that can meet the privacy protection requirements of manufacturing systems. Therefore, an integrated decision-making framework called Pythagorean fuzzy-entropy-rank sum-Combined Compromise Solution (PF-entropy-RS-CoCoSo) is developed in this study, including two main phases. In the first phase, the PF-entropy-RS method is applied to obtain the subjective and objective weights of criteria to evaluate the technical challenges of transforming blockchain technology for a sustainable manufacturing paradigm in the Industry 4.0 era. The PF-CoCoSo model is then utilized in the second phase to assess the preferences of organizations over different technical challenges of the blockchain technology transformation for the sustainable manufacturing paradigm in the Industry 4.0 era. An empirical case study is taken to assess the main technical challenges of blockchain technology transformation for the sustainable manufacturing paradigm. Furthermore, a comparison analysis and a sensitivity investigation are made to demonstrate the superiority of the developed framework. © 2023 Elsevier Inc.
Breast tumor localization and segmentation using machine learning techniques: overview of datasets, findings, and methods
(Elsevier, 2023) Ranjbarzadeh, Ramin; Dorosti, Shadi; Jafarzadeh Ghoushchi, Saeid; Caputo, Annalina; Tirkolaee, Erfan Babaee; Ali, Sadia Samar; Arshadi, Zahra; Bendechache, Malika
The Global Cancer Statistics 2020 reported breast cancer (BC) as the most common diagnosis of cancer type. Therefore, early detection of such type of cancer would reduce the risk of death from it. Breast imaging techniques are one of the most frequently used techniques to detect the position of cancerous cells or suspicious lesions. Computer-aided diagnosis (CAD) is a particular generation of computer systems that assist experts in detecting medical image abnormalities. In the last decades, CAD has applied deep learning (DL) and machine learning approaches to perform complex medical tasks in the computer vision area and improve the ability to make decisions for doctors and radiologists. The most popular and widely used technique of image processing in CAD systems is segmentation which consists of extracting the region of interest (ROI) through various techniques. This research provides a detailed description of the main categories of segmentation procedures which are classified into three classes: supervised, unsupervised, and DL. The main aim of this work is to provide an overview of each of these techniques and discuss their pros and cons. This will help researchers better understand these techniques and assist them in choosing the appropriate method for a given use case. © 2022 Elsevier Ltd
Intuitionistic fuzzy power aczel-alsina model for prioritization of sustainable transportation sharing practices
(Elsevier, 2022) Senapati, Tapan; Simic, Vladimir; Saha, Abhijit; Dobrodolac, Momcilo; Rong, Yuan; Tirkolaee, Erfan Babaee
Traffic congestion and environmental pollution generated by transportation activities significantly endanger the sustainable development of cities. This study presents a new strategy for implementing the concept of shared mobility, where postal operators use their widespread networks of units and serve as service providers. To enhance its real-world implementation, four sustainable transportation sharing practices are elaborated. The key question is how to identify the most sustainable alternative that should be offered by service providers. To resolve this challenge, this paper develops an advanced decision support model based on Aczel-Alsina aggregation operators and power operators within the intuitionistic fuzzy (IF) environment. The criteria weights are determined through the Shannon entropy-based power weighted method. Aczel-Alsina operations for IF numbers are proposed to aggregate the decision information. In light of these operational laws, two IF Aczel-Alsina aggregation operators and their enviable characteristics are provided. These advanced aggregation operators are used to formulate the IF power Aczel-Alsina model. The case study of the city of Novi Sad illustrates its applicability. According to the research findings, it is recommended that the public postal operator invests in a sharing e-bicycle fleet. The comparative investigation demonstrates the superiority of the developed decision support model. Its major strengths are simple calculation and fast information processing. © 2022 Elsevier Ltd
A tree augmented naive bayes-based methodology for classifying cryptocurrency trends
(ELSEVIER, 2023) Dağ, Ali; Dağ, Aslı Z.; Asilkalkan, Abdullah; Şimşek, Serhat; Delen, Dursun
As the popularity of blockchain technology and investor confidence in Bitcoin (BTC) increased in recent years, many individuals started making BTC and other cryptocurrency investments, in expectation of high returns. However, as recent market movements have shown, the lack of regulation and oversight makes it difficult to guard against high volatility and potentially significant losses in this sector. In this study, we propose a datadriven Tree Augmented Naive (TAN) Bayes methodology that can be used for identifying the most important factors (as well as their conditional, interdependent relationships) influencing BTC price movements. As the model is parsimonious without sacrificing accuracy, sensitivity, and specificity-as evident from the average accuracy value-the proposed methodology can be used in practice for making short-term investment decisions.
A sustainable-circular citrus closed-loop supply chain configuration: pareto-based algorithms
(ELSEVIER, 2023) Goodarzian,Fariba; Ghasemi,Peiman; Gonzalez,Ernesto DR. Santibanez; Tirkolaee, Erfan Babaee
Configuration of sustainable supply chains for agricultural products has been a well-known research field recently which is continuing to evolve and grow. It is a complex network design problem, and despite the abundant literature in the field, there are still few models offered to integrate social impacts and environmental effects to support network design decision-making to support the configuration of the citrus supply chain. In this work, the citrus supply chain design problem is investigated by integrating the production, distribution, inventory control, recycling and locational decisions in which the triple bottom lines of sustainability, as well as circularity strategy, are addressed. Accordingly, a novel multi-objective Mixed-Integer Linear Programming (MILP) model is proposed to formulate a multi-period multi-echelon problem to design the sustainable citrus Closed-Loop Supply Chain (CLSC) network. To solve the developed model, the ?-constraint approach is employed in small-sized problems. Furthermore, Strength Pareto Evolutionary Algorithm II (SPEA-II) and Pareto Envelope-based Selection Algorithm II (PESA-II) algorithms are used in medium- and large-sized problems. Taguchi design technique is then utilized to adjust the parameters of the algorithms efficiently. Three well-known assessment metrics and convergence analysis are regarded to test the efficiency of the suggested algorithms. The numerical results demonstrate that the SPEA-II algorithm has a superior efficiency over PESA-II. Moreover, to validate the applicability of the developed methodology, a real case study in Mazandaran/Iran is investigated with the help of a set of sensitivity analyses.
A mathematical model to investigate the interactive effects of important economic factors on the behaviors of retailers
(SPRINGER, 2022) Khakbaz, Amir; Tirkolaee, Erfan Babaee
Cost management is a key step to the success of any logistics system and supply chain management. Inventory costs are an important part of logistics costs which are highly affected by economic factors such as demand growth rate (DGR), interest rate (ir), and inflation rate (e). Analyzing the interactive effects of these economic factors plays a key role in preventing failures of logistics systems This study aims to develop a novel mathematical model and investigate the interactive effects of these factors on the behavior of retailers in Iran. To the best of our knowledge, this is the first time that the sale price is defined as a function of time and inflation rate where the demand rate is built up with a linear function of time. Different scenarios and sub-scenarios are then taken into consideration based on different combinations of factors and assumptions. As the main findings of the study, it is revealed that if e < 18% or ir > 40.52%, holding costs are much higher than buying costs, and retailers are reluctant to invest in inventories. Given that DGR is independent of the inflation rate, and also if e > 20.45% or ir < 31.9%, then DGR fluctuations have no impact on the total cost. Hence, in this case, buying costs are much higher than holding costs, and retailers are eager to invest in inventories instead of bank deposits. Furthermore, it is concluded that decision-makers can use the interest rate as leverage to set the probability of shortages and hoardings. Finally, some useful future research directions are discussed based on the main limitations of the study.
Optimal matchday schedule for Turkish professional soccer league using nonlinear binary integer programming
(RAMAZAN YAMAN, 2022) Göçgün, Yasin; Bakır, Niyazi Onur
Sports scheduling problems are interesting optimization problems that require the decision of who play with whom, where and when to play. In this work, we study the sports scheduling problem faced by the Turkish Football Federation. Given the schedule of games for each round of the season, the problem is to determine the match days with the goal of having a fair schedule for each team. The criteria we employ to establish this fairness are achieving an equal distri-bution of match days between the teams throughout the season and the ideal assignment of games to different days in each round of the tournament. The problem is formulated as a nonlinear binary integer program and is solved op-timally for each week. Our results indicate that significant improvements over the existing schedule can be achieved if the optimal solution is implemented.
Identifying and prioritizing resilient health system units to tackle the COVID-19 pandemic
(Elsevier Ltd., 2022) Adabavazeh, Nazila; Nikbakht, Mehrdad; Tirkolaee, Erfan Babaee
Since human health greatly depends on a healthy and risk-free social environment, it is very important to have a concept to focus on improving epidemiology capacity and potential along with economic perspectives as a very influential factor in the future of societies. Through responsible behavior during an epidemic crisis, the health system units can be utilized as a suitable platform for sustainable development. This study employs the Best-Worst Method (BWM) in order to develop a system for identifying and ranking health system units with understanding the nature of the epidemic to help the World Health Organization (WHO) in recognizing the capabilities of resilient health system units. The purpose of this study is to identify and prioritize the resilient health system units for dealing with Coronavirus. The statistical population includes 215 health system units in the world and the opinions of twenty medical experts are also utilized as an informative sample to localize the conceptual model of the study and answer the research questionnaires. The resilient health system units of the world are identified and prioritized based on the statistics of “Total Cases”, “Total Recovered”, “Total Deaths”, “Active Cases”, “Serious”, “Total Tests” and “Day of Infection”. The present descriptive cross-sectional study is conducted on Worldometer data of COVID-19 during the period of 17 July 2020 at 8:33 GMT. According to the results, the factors of “Total Cases”, “Total Deaths”, “Serious”, “Active Cases”, “Total Recovered”, “Total Tests” and “Day of Infection” are among the most effective ones, respectively, in order to have a successful and optimal performance during a crisis. The attention of health system units to the identified important factors can improve the performance of epidemiology system. The WHO should pay more attention to low-resilience health system units in terms of promoting the health culture in crisis management of common viruses. Considering the importance of providing health services as well as their significant effect on the efficiency of the world health system, especially in critical situations, resilience analysis with the possibility of comparison and ranking can be an important step to continuously improve the performance of health system units.
Modelling joint deterioration in roller compacted concrete pavement
(Springer, 2022) Mohammed, Haneen; Abed, Ahmed; Thom, Nick
Joints in Roller Compacted Concrete (RCC) pavements are used to distribute traffic loading between adjacent slabs by friction. The Load Transfer Stiffness (LTS) of the joints has critical effects on RCC pavement performance near the joints. Research has shown that LTS can deteriorate over time due to traffic loading or environmental conditions. This study investigates the deterioration of LTS of RCC pavement joints and its effect on the fatigue cracking performance near the joints. To achieve that, first, an innovative experimental programme was designed to measure LTS as a function of number of load repetition, joint width, and RCC mix properties using a cyclic shear test setup. Second, a mathematical model was derived to predict LTS deterioration in joints. This model was validated against the experimental data. Lastly, an RCC pavement design model was developed using the LTS deterioration model. To demonstrate the application of the developed solution, a hypothetical RCC pavement structure consisting of four slabs was considered. The analysis results show that LTS has inverse relationship and direct impact of fatigue life of RCC. In particular, the results demonstrate that fatigue damage over an analysis period of 20 years is negligible if LTS is assumed constant, which is unrealistic, but it can reach 40% if LTS deterioration is considered in the analysis. Accordingly, this study recommends considering the deterioration of RCC joint LTS when design that kind of pavement structures. © 2022, The Author(s), under exclusive licence to Chinese Society of Pavement Engineering.
A hybrid biobjective markov chain based optimization model for sustainable aggregate production planning
(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2022) Tirkolaee, Erfan Babaee; Aydın, Nadi Serhan; Mahdavi, Iraj
This research addresses the sustainable aggregate production planning problem by considering the outsourcing option and workforce skill levels as well as taking a Markov process approach for the inventory level. For this purpose, a hybrid biobjective mixed-integer nonlinear programming model featuring a continuous-time Markov chain to accommodate the inventory decision process is developed. The proposed Markov chain approach efficiently describes system dynamics modeling of the production system through a stochastic process. The objective functions are to minimize total cost and total environmental pollution at the same time. To validate the applicability of the methodology and to evaluate the model complexity, three numerical examples are generated based on one of the previous studies in the literature. It is demonstrated that the suggested methodology is able to come up with the final feasible solution based on optimal inventory decisions in less than 65 s. Finally, a number of sensitivity analyses are presented to study the behavior of the objectives under real-world instability and discuss the practical implications and managerial insights. As one of the main findings, it is revealed that the objective functions have no sensitivity to some change intervals of the parameters, which can be analyzed more earnestly by the management in case of the resource allocation process.
A socio-economic optimization model for blood supply chain network design during the COVID-19 pandemic: an interactive possibilistic programming approach for a real case study
(Elsevier Ltd., 2022) Tirkolaee, Erfan Babaee; Golpîra, Hêris; Javanmardan, Ahvan; Maihami, Reza
In uncertain circumstances like the COVID-19 pandemic, designing an efficient Blood Supply Chain Network (BSCN) is crucial. This study tries to optimally configure a multi-echelon BSCN under uncertainty of demand, capacity, and blood disposal rates. The supply chain comprises blood donors, collection facilities, blood banks, regional hospitals, and consumption points. A novel bi-objective Mixed-Integer Linear Programming (MILP) model is suggested to formulate the problem which aims to minimize network costs and maximize job opportunities while considering the adverse effects of the pandemic. Interactive possibilistic programming is then utilized to optimally treat the problem with respect to the special conditions of the pandemic. In contrast to previous studies, we incorporated socio-economic factors and COVID-19 impact into the BSCN design. To validate the developed methodology, a real case study of a Blood Supply Chain (BSC) is analyzed, along with sensitivity analyses of the main parameters. According to the obtained results, the suggested approach can simultaneously handle the bi-objectiveness and uncertainty of the model while finding the optimal number of facilities to satisfy the uncertain demand, blood flow between supply chain echelons, network cost, and the number of jobs created.
A machine learning framework for assessing the risk of venous thromboembolism in patients undergoing hip or knee replacement
(SPRINGERNATURE, 2022) Dezfouli, Elham Rasouli; Delen, Dursun; Zhao, Huimin; Davazdahemami, Behrooz
Venous thromboembolism (VTE) is a well-recognized complication that is prevalent in patients undergoing major orthopedic surgery (e.g., total hip arthroplasty and total knee arthroplasty). For years, to identify patients at high risk of developing VTE, physicians have relied on traditional risk scoring systems, which are too simplistic to capture the risk level accurately. In this paper, we propose a data-driven machine learning framework to identify such high-risk patients before they undergo a major hip or knee surgery. Using electronic health records of more than 392,000 patients who undergone a major orthopedic surgery, and following a guided feature selection using the genetic algorithm, we trained a fully connected deep neural network model to predict high-risk patients for developing VTE. We identified several risk factors for VTE that were not previously recognized. The best FCDNN model trained using the selected features yielded an area under the ROC curve (AUC) of 0.873, which was remarkably higher than the best AUC obtained by including only risk factors previously known in the medical literature. Our findings suggest several interesting and important insights. The traditional risk scoring tables that are being widely used by physicians to identify high-risk patients are not considering a comprehensive set of risk factors, nor are they as powerful as cutting-edge machine learning methods in distinguishing low- from high-risk patients
A data preparation framework for cleaning electronic health records and assessing cleaning outcomes for secondary analysis
(Elsevier Ltd, 2023) Miao, Zhuqi; Sealey, Meghan D.; Sathyanarayanan, Shrieraam; Delen, Dursun; Zhu, Lan; Shepherd, Scott
Even though data preparation constitutes a large proportion of the total effort involved in electronic health record (EHR) based secondary analysis, guidelines for EHR data preparation are still insufficient to date. This study proposes a data preparation framework that can guide and validate the cleaning of EHRs for secondary analysis. The developed framework consists of three core themes—workflow, assessment and cleaning methods, and cleaning evaluation scheme. To illustrate the viability of the proposed framework, we applied it to a hip-fracture readmission scenario using the underlying data extracted from a large EHR database. The case study demonstrated the effectiveness of the proposed framework in organizing and standardizing phases and processes within an EHR data preparation workflow. Furthermore, the cleaning evaluation scheme was found to be effective in validating EHR cleaning methods, especially for those used to handle complex issues that usually appear in patient demographics, longitudinal attributes of EHRs, and the application of filtering and imputation cleaning methods.
A parallel hybrid PSO-GA algorithm for the flexible flow-shop scheduling with transportation
(Elsevier Ltd, 2022) Amirteimoori, Arash; Mahdavi, Iraj; Solimanpur, Maghsud; Ali, Sadia Samar; Tirkolaee, Erfan Babaee
In this paper, a Mixed-Integer Linear Programming (MILP) model to simultaneously schedule jobs and transporters in a flexible flow shop system is suggested. Wherein multiple jobs, finite transporters, and stages with parallel unrelated machines are considered. In addition to the mentioned technicalities, the jobs are able to omit one or more stages, and may not be executable by all the machines, and similarly, transportable by all the transporters. To the best of our knowledge, no study in the literature has featured efficacy of the parallel computing in simultaneous scheduling of jobs and transporters in the flexible flow shop system which remarkably shortens run time if the solution approaches are designed accordingly. To this end, we employ Gurobi solver, Parallel Genetic Algorithm (PGA), Parallel Particle Swarm Optimization (PPSO) and hybrid Parallel PSO-GA Algorithm (PPSOGA) to deal with the problem instances. Furthermore, a parallel version of Ant Colony Optimization (ACO) algorithm adapted from the state-of-the-art literature is developed to verify the performance of our suggested solution methods. Using 60 problem instances generated via uniform distribution, the suggested solution approaches are compared against one another. After assessing the results of the computational experiments, it is deduced that PPSOGA algorithm outperforms PGA, PPSO, Parallel Ant Colony Optimization (PACO) and Gurobi solver in terms of the quality of the solutions. The efficiency and run time of the suggested approaches are then assessed through two prominent statistical tests (i.e., Wald and Analysis of Variance (ANOVA)). Eventually, it comes to spotlight that PPSOGA algorithm is computationally rewarding and dependable.
Performance evaluation of omni-channel distribution network configurations considering green and transparent criteria under uncertainty
(MDPI, 2022) Babaei, Ardavan; Khedmati, Majid; Jokar, Mohammad Reza Akbari; Tirkolaee, Erfan Babaee
Satisfying customer demand is one of the growing concerns of supply chain managers. On the other hand, the development of internet communications has increased online demand. In addition, the COVID-19 pandemic has increased the demand for online shopping. One of the useful concepts that help to address this concern is the omni-channel strategy, which integrates online and traditional channels with the aim of improving customer service level. For this purpose, this paper proposes an algorithm for evaluating Omni-channel Distribution Network Configurations (OCDNCs). The algorithm applies an extended Data Envelopment Analysis (DEA) model to evaluate OCDNCs based on cost, service, transparency, and environmental criteria; and then, forms a consensus on the evaluation results generated according to different criteria by utilizing an uncertain optimization model. To the best of our knowledge, this is the first attempt in which such an algorithm has been employed to take into account the mentioned criteria in a model to evaluate OCDNCs. The application of the proposed models was investigated in a case study in relation to the Indian retail industry. The results show that the configuration with the most connections among its members was the most stable, robust, and efficient.
A fuzzy Einstein-based decision support system for public transportation management at times of pandemic
(Elsevier, 2022) Deveci, Muhammet; Pamucar, Dragan; Gokaşar, Ilgın; Delen, Dursun; Martínez, Luis
Optimal decision-making has become increasingly more difficult due to their inherent complexity exacerbated by uncertain and rapidly changing environmental conditions in which they are defined. Hence, with the aim of improving the uncertainty management and facilitating the weighting criteria, this paper introduces an improved fuzzy Einstein Combined Compromise Solution (CoCoSo) method- ology. Such a CoCoSo model improves previous CoCoSo proposals by using nonlinear fuzzy weighted Einstein functions for defining weighted sequences. In addition, it proposes a novel algorithm for determining the criteria weights based on the fuzzy logarithmic function, therefore it allows decision- makers a better perception of the relationship between the criteria, as it considers the relationships between adjacent criteria; high consistency of expert comparisons; and enables the definition of weighting coefficients of a larger set of criteria, without the need to cluster (group) the criteria. Nonlinear fuzzy Einstein functions implemented in the fuzzy Einstein CoCoSo methodology enable the processing of complex and uncertain information. Such characteristics contribute to the rational definition of compromise strategies and enable objective reasoning when solving real-world decision problems. The efficiency, effectiveness, and robustness of the proposed fuzzy Einstein CoCoSo model are illustrated by a case study to create a conceptual framework to evaluate and rank the prioritization of public transportation management at the time of the COVID-19 pandemic. The results reveal its good performance in determining the transportation management systems strategy.
The attitudes of the telecommunication customers in the COVID-19 outbreak: The effect of the feature selection approach in churn analysis
(TR- Dizin, 2022) Donat, Handan; Karadayı Usta, Saliha
Today's rising cutting-edge technology requirements and competitive environment in telecommunication industry has gained a remarkable importance due to the COVID-19 pandemics in terms of high need of information sharing and remote communication necessity. Telecommunication companies conduct significant analyses by highlighting that the customer data is the most valuable information. Besides, they obtain results emphasizing that acquiring new customers is costlier than retaining the existing ones. Therefore, the companies are willing to determine the important customer features in order to understand why they shift to the other telecommunication service providers. Hence, this study aims to conduct a churn analysis by feature selection approach with large volumes of telecommunication customer data in order to present what kind of customer behaviors and qualifications exist. Since there is a huge amount of data in this field, data mining is a vital requirement. The performance outputs were observed, and the features carrying these outputs to the highest value were identified. The data collection and analysis were carried out in mid-2019, and the same data collection and analysis were carried out again at the beginning of 2021, and these before and after results were compared. In addition, a comparison was made with the results obtained by the other churn analysis studies. This paper contributes to the practitioners by presenting the most important customer features in telecom customer churn, and a new approach in performance evaluation have been proposed specific to the telecommunication market with the industry experts’ guidance as a theoretical contribution.
Modelling and predicting the growth dynamics of Covid-19 pandemic: A comparative study including Turkey
(DergiPark, 2022) Tirkolaee, Erfan Babaee; Aydin, Nadi Serhan
Estimating the growth dynamics of a pandemic is critical for policy makers to fine-tune emergency policies in health and other public sectors. The paper presents country-level calibration and prediction results on some well-known models in the literature, namely, the logistic, exponential, Gompertz, SIR and SEIR models. The models are implemented on real data from various countries, including Turkey, and their performance for different estimation windows have been analyzed using R2 scores. The computational results are obtained using Python. The Gompertz model outperforms other models by consistently offering a better fit for the total number of infected. The exponential model is helpful in describing the growth dynamics in the early stages of the COVID-19 pandemic. Suspected-Infected-Recovered (SIR) and Susceptible-Exposed-Infectious-Removed (SEIR) models display a fair performance on the underlying active cases data in many circumstances. Quantitative models can offer policy makers in Turkey and elsewhere a better insight on the evolution of pandemic when everything else is held constant and the infections follow a typical path. The results can be highly sensitive to changes in policies. There is not a single model that can perfectly mimic all stages of pandemic. An ensemble model or multi-modal distributions can be used to capture the evolution of multi-wave pandemics.

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