Endüstri Mühendisliği Bölümü Koleksiyonu

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https://hdl.handle.net/20.500.12846/478

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Investigation of Green Criteria With Clustering Analysis in Green Supplier Selection
(IGI Global Publisher, 2021) Taş, Mehmet Ali
The rise of technology in human culture has changed almost every facet of society. Technology is especially useful regarding sustainable development. These technologies can cause significant greenhouse gas reductions and other benefits in terms of logistics and smart cities. New technology applied in this way can greatly help the human effort to restore the environment. Disruptive Technologies and Eco-Innovation for Sustainable Development provides an in-depth look into the new techniques, strategies, and technologies for achieving environmental sustainability through best business and technology practices. The book covers topics such as eco-innovation, green criteria, Agriculture 4.0, and topics related to logic, philosophy, and history of science and technology from the green/sustainable point of view. It is essential for managers, academicians, scientists, students, and researchers in various government, public, and private sectors.
A Systematic Literature Review Of Green Supplier Selection Practices
(Akademisyen Yayınevi, 2021) Taş, Mehmet Ali; Akcan, Serap
The supply chains established by the enterprises to continue their activities are turning into green supply chains with the change in the environmental perspective. Identifying suppliers is a difficult problem when establishing green supply chains. Multi-criteria decision-making (MCDM) methods (AHP, TOPSIS, VIKOR, etc.) can be used to solve these problems (Akcan & Taş, 2019). In addition to these, various methods can be implemented to make this decision. Opinions of different numbers of experts (decision-makers) can be consulted, the number of alternative suppliers taken into consideration may vary according to the problem addressed, etc. For this reason, it is seen that many parameters are involved in green supplier selection practices.
A Hybrid Fuzzy MCDM Approach for Sustainable Health Tourism Sites Evaluation
(IGI Global, 2022) Taş, Mehmet Ali
Fuzzy logic, which is based on the concept of fuzzy set, has enabled scientists to create models under conditions of imprecision, vagueness, or both at once. As a result, it has now found many important applications in almost all sectors of human activity, becoming a complementary feature and supporter of probability theory, which is suitable for modelling situations of uncertainty derived from randomness. Fuzzy mathematics has also significantly developed at the theoretical level, providing important insights into branches of traditional mathematics like algebra, analysis, geometry, topology, and more. With such widespread applications, fuzzy sets and logic are an important area of focus in mathematics. The Handbook of Research on Advances and Applications of Fuzzy Sets and Logic studies recent theoretical advances of fuzzy sets and numbers, fuzzy systems, fuzzy logic and their generalizations, extensions, and more. This book also explores the applications of fuzzy sets and logic applied to science, technology, and everyday life to further provide research on the subject. This book is ideal for mathematicians, physicists, computer specialists, engineers, practitioners, researchers, academicians, and students who are looking to learn more about fuzzy sets, fuzzy logic, and their applications.
Performance Evaluation of Green Ports via C-IFS AHP: A Case Study in Turkey
(Springer Nature Switzerland AG, 2024) Taş, Mehmet Ali; Çakır, Esra
Seaports are an indispensable element of world maritime trade. All activities in the world that deal with problems such as global warming and the deteriorating ecological balance should prioritize the environment and be sustainable. The concept of the green port has inherently emerged in this direction. Various indicators can be used to assess the environmental performance of ports in a country. Seaports evaluated according to these indicators can obtain the green port title and certification. Seaports with the highest environmental sustainability performance can be scored and ranked according to the weights of the indicators. Fuzzy multi-criteria decision making (MCDM) methods are useful in calculating the importance of these indicators and ranking the alternative ports. Linguistic views and responses of experts can be placed as data for the proposed MCDM with a fuzzy approach. In this study, a novel methodology including circular intuitionistic fuzzy sets (C-IFS), which is newly introduced to the literature, and Analytic Hierarchy Process (AHP) approach is proposed. It is aimed that the criteria weights and alternative ranking obtained as a result of this study contribute to the appraisement of the seaports in terms of environmental sustainability and enable the emergence of the aspects that are required to be improved.
Integrated Fuzzy Multi-Criteria Decision Making Application within An Environmental Evaluation Framework: A Case Study in Türkiye
(2024) Taş, Mehmet Ali; Yetgin, Serap Akcan
The selection process of eligible suppliers in supply chains entails numerous challenges under rapidly evolving conditions. Environmental considerations in public discourse, competitive market structures, and emerging technological capabilities influence the decision-making procedures. Rather than the conventional criteria of cost and service, different criteria have more recently been taken into consideration. In this study, presenting a Turkish case study, environmental management, environmental agility and environmental technology dimensions and the criteria related to these dimensions are defined. A fuzzy SWARA-BWM method was implemented in an integrated way to cope with the supplier selection problem. Different scenarios were created and benchmarked. The results of the study indicate that environmental agility is the prominent dimension, while the most significant criterion is delivery speed. The optimal supplier alternative among the four alternatives was identified as A3. This study was carried out to contribute to the examination and modeling of supply chain management issues.
Role of Internet of Things in Supply Chain Management
(Serüven Publishing, 2022) Aylak, Batin Latif
The emergence of supply chain concepts has been observed since the 1980s (Giordano-Spring, S. and Works, S., 2006); the emphasis was on the abolition of old concepts centered on constrained logistics such as transportation and warehousing. Supply chain management is a critical part of innovation in managing materials, information flows, and finances (Sun, W. et al., 2022), from the initial stage of giving raw materials to the supplier to the final delivery to the end consumer. Supply chain management and its 6 stages are provided in the figure below. Supply chain management is a complete package involving management activities for following the products, items, or services in a company or an organization from raw material to manufacturing company, the finished products to distribution, and the final consumer in such a way that the cost gets minimized. (Kothari, S. et al., 2018). As a result, it is accurate to say that supply chain activities cover all facets of the development of product logistics, including tasks related to manufacturing and production, sourcing and transportation, inventory management, warehouse management, and shipping. Over the years, the global supply chain system phone has benefited from lower wages as well as the purchase of inexpensive raw materials from some countries (Kothari, S. et al., 2018)
Route optimization for the maintenance of wind power plants by using metaheuristic algorithms
(Livre de Lyon, 2022) Özdemir, Mehmet Hakan; Çakıroğlu, Celal; Aylak, Batin Latif; Taş, Mehmet Ali
Energy plays a crucial role in economic and social development (Ur Rehman et al., 2019). Countries depend on energy from various sources for their economic power (Kaplan and San, 2011). In addition, increasing production in response to increasing consumption all over the world has boosted energy demands (Siddiqui et al., 2019). Concurrently, interest in renewable energy sources has been growing in recent years due to global pollution created by traditional energy sources reaching concerning levels (Yong et al., 2016). The use of sustainable and green energy generated from solar, wind, biomass and geothermal energy sources has increased. Through the use of renewable energy sources, adverse impacts on the environment and climate can be mitigated (Mizsey and Racz, 2010). The widespread use of environmentally friendly technologies helps to reduce the use of energy and resources in industrial production as well as the waste generated as a result (Dovì et el., 2009).
Applicaitons of ultralight overhead conveyor systems in the logistics sector
(Serüven Publishing, 2023) Aylak, Batin Latif
In today’s fast-paced and ever-evolving logistics sector, the efficient movement of goods plays a pivotal role in ensuring timely and cost-effective delivery. Traditional material handling methods such as manual labor, forklifts, and conventional conveyor systems have limitations that can hinder productivity and flexibility (Myhre, Transeth, & Egeland, 2015). However, with advancements in technology, ultralight overhead conveyor systems have emerged as a promising solution to overcome these challenges. This chapter work explores the applications of ultralight overhead conveyor systems in the logistics sector, focusing on their benefits, implementation challenges, and potential future developments.
Warehouse layout optimization using association rules
(2022) Aylak, Batin Latif
The enormous growth in the online retailing has enhanced the significance of logistic processes and warehouse technology. Therefore, this study proposes a strategy for the preparation of an optimal warehouse layout, which allows the minimization of the transport and the minimization of the injury risk. The dataset utilized in this study is based on the product orders of a supplier company. An association rule analysis was used to group the frequently co-occurring products in the dataset. The apriori and FPgrowth algorithm were chosen to perform the association rule analysis. A comprehensive analysis of the orders in the period 2015-20 indicates variations in the product grouping over the years. Based on the results of this study, the warehouse layout can be continuously improved depending on the customer orders.
Türkiye’de lojistik sektöründe faaliyet gösteren işletmelerin dijital trendlerinin incelenmesi
(2020) Aylak, Batin Latif; Kayıkcı, Yaşanur; Taş, Mehmet Ali
Endüstri 4.0’la beraber her sektörde yeni uygulamalar geliştirilmeye başlanmıştır. Bu uygulama alanlarından bazıları; tedarik zinciri geliştirmeleri, rota optimizasyonları, Büyük Veri kullanımı, yapay zeka geliştirilmesi, akıllı depo tasarımları, robotlaşma ve otomasyon, sürücüsüz araçların hem üretim hem de sektör hizmetlerindeki gelişimidir. Tüm bu geliştirme ve teknolojiler sektörde dijitalleşme süreci gerektirmektedir. Bu çalışma Endüstri 4.0’la oluşan değişim rüzgarlarının Türkiye’deki lojistik sektöründe hangi trendleri yarattığına odaklanmaktadır. Yapılan literatür taraması, röportaj analizleri ve teknoloji, lojistik, servis ve IT tedariki, perakende gibi farklı sektörlerden 65 şirketin katılımıyla yapılan anket çalışması analizi sonucunda, 2017 ve sonrasında Türkiye lojistik sektöründeki trendlerin Supergrid Lojistik, otonom lojistik, robotik ve otomasyon, Nesnelerin İnterneti, Bulut Lojistik, Büyük Veri ve e-ticaret şeklinde sıralanabileceği sonucuna varılmıştır.
The Impacts of the Applications of Artificial Intelligence in Maritime Logistics
(2022) Aylak, Batin Latif
This study aims to identify current approaches in the usage of Artificial Intelligence (AI) methods for solving shipping problems. Recent advances in AI are being examined, and the way it is adapted to maritime logistics is reviewed. In this study, 66 papers dealing with AI in the maritime industry are reviewed bibliometrically. Research data were primarily sourced from databases of IEEE Xplore, Web of Science, ScienceDirect (Elsevier), Sciences Citation Index, Google Scholar, Springer, and journals. Selected papers are categorized and classified, and the outcomes of some noteworthy publications are discussed in detail. A comprehensive assessment is also presented, which highlights research gaps and forecasts future research orientations. Two possible areas in the maritime industry are proposed for further research using AI capabilities. Predictive analysis is the first domain, followed by energy efficiency optimization. In addition, Machine Learning (ML) and Operations Research (OR) have fostered a growing interest in automating the learning of heuristics to solve optimization problems to avoid the need for expensive and inefficient human labour to create highly specialized heuristics. Future research can take advantage of these new ML approaches to address Maritime Logistics problems utilizing the ever-increasing amount of data available. Future research on maritime logistics can also develop learning models based on the identified gaps.
The Effects of the Applications of Blockchain Technology on the Logistics sector
(2022) Aylak, Batin Latif
Logistics is a combination of businesses involving various activities and processes which generate value by goods and services. Maintaining track of all transactions is a crucial task in logistics. Logistics 4.0 enables the optimum synchronization of activities inside corporate boundaries; if effective, logistical constraints resulting from industrial sender and receiver channels may be considerably alleviated. Blockchain paves the way for implementation of smart logistics. It served as storage for the transactions after distributed ledger technologies was implemented before the digital cryptocurrency a year ago. It is a decentralized system based on five essential principles: decentralization, P2P, transparency with privacy protection, and algorithmic logic. Some logistics companies are already employing block chain. This paper summarizes adoption of distributed ledger technology in logistics is being investigated. Furthermore, the strengths and weakness of blockchain in logistic industry were extracted from recent scientific literature for readers to overview the application of technology. It is reported that blockchain adoption provides immutability, data security, tracking, storage, dependability, and cost-effective alternatives in logistics industry. However, there are a few obstacles that prevent full-scale adaption by many logistics and transportation sectors, such as throughput, and latency constraints. Nodes aren’t monitored by centralized entity to notify security breach, so data security may be compromised. Furthermore, the blockchain is still in its infancy; there’s no single standard, theories are difficult to grasp, and even the most basic types of application need programmer assistance.
Prediction of Cumulative Installed Power of Geothermal Power Plants in Turkey by Using Artificial Neural Network and Bidirectional Long Short-Term Memory
(2022) Özdemir, Mehmet Hakan; Aylak, Batin Latif
Turkey has a great potential for renewable energies. The number of power plants (PP) producing electricity from renewable energy sources and accordingly the installed power has risen over the years. As of the end of December 2021, the cumulative installed power of Turkey reached 99819.6 MW and the share of the total installed power of the PPs generating electricity from renewable energy sources was 53.72%. Although the installed power has increased, the percentage of PPs using renewable energy sources in total electricity generation is not yet at the desired level. However, geothermal energy is being used more and more in electricity generation alongside the other most well-known types of renewable energy. It can be observed that the installed power of geothermal power plants (GPP) in Turkey started to increase gradually after 2007, and as of the end of December 2021, the cumulative installed power reached 1676.2 MW. In this study, with the data for the cumulative installed power of GPPs in Turkey in the 2007-2021 period, the cumulative installed power of GPPs in Turkey for 2022 was predicted by using Artificial Neural Network (ANN) and Bidirectional Long Short Term Memory (BLSTM) methods, and the results were compared and interpreted.
Impacts of sustainability on supply Chain management
(2022) Aylak, Batin Latif
Nowadays, environmental strategies are critical for improving organizational operations. Therefore, sustainable supply chain management plays a key role in supporting organizations improve their overall performance. This paper seeks to determine the level of correlation between sustainability practices and supply chain management within diverse organizational contexts. The research study was facilitated through a systematic review approach. It was based on a data pool consisting of 13 secondary sources that were obtained from online research databases such as Web of Science and Scopus. The sources were subsequently subjected to the exclusion and inclusion criteria. The results obtained indicated that sustainability practices can have a can have a direct and statistically significant impact on organizational operations such as procurement, manufacturing, and logistics, which fall under supply chain management functions. However, it is unclear, whether sustainability practices impacts procurement or logistics processes more. The findings further implied that the implementation of sustainability practices at an organizational level can result in the establishment of novel linkages with partners in the supply chain. The linkages result from the necessity of forming relationships with additional suppliers to obtain relevant resources.
Estimation of wind speed probability distribution parameters by using four different metaheuristic algorithms
(2022) Oral, Okan; İnce, Murat; Aylak, Batin Latif; Özdemir, Mehmet Hakan
The inclusion of energy produced from renewable energy sources (RES) such as solar and wind energy into existing energy systems is important to reduce carbon emissions, air pollution and climate change, and to ensure sustainable development. However, the integration of RES into the energy system is quite difficult due to their highly uncertain and intermittent nature. In this study, considering three different probability density functions (PDFs) in total, the scale and shape parameters of the Weibull PDF, the scale parameter of the Rayleigh PDF, and the scale and shape parameters of the Gamma PDF were estimated for the wind speed data obtained from urban stations located in Istanbul by using the four different metaheuristic algorithms, namely Genetic Algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) algorithms. Calculating the mean absolute error (MAE), root mean squared error (RMSE), and R2 values for each PDF at each station, the PDF that characterizes the wind speed probability distribution the best was identified.
Predicting world electricity generation by sources using different machine learning algorithms
(2024) Özdemir, Mehmet Hakan; Aylak, Batin Latif; Oral, Okan; İnce, Murat
Electrical energy plays a crucial role in both social and economic growth. It is thought to be an essential part of industrial manufacturing. In addition to its contribution to industry, electrical energy is essential for addressing the needs of people on a daily basis. Therefore, electricity generation prediction is crucial for accurate electricity planning and energy usage, with machine learning (ML) algorithms becoming popular for their ability to extract complex relationships and make precise predictions. With the data from the period 2000-2022, this study predicts world electricity generation for 2023 by different energy sources employing seven different ML algorithms, namely long short-term memory (LSTM), artificial neural network (ANN), linear regression (LR), support vector regression (SVR), decision tree regression (DTR), random forest regression (RFR) and eXtreme gradient boosting (XGBoost). The algorithms were also contrasted in the study, and it was discovered that LSTM produced the most accurate predictions. [Received: June 16, 2023; Accepted: August 19, 2023]
Estimation of tree height with machine learning techniques in coppice-originated pure sessile oak (Quercus petraea (Matt.) Liebl.) stands
(2023) Şahin, Abbas; Özdemir, Gafura Aylak; Oral, Okan; Aylak, Batin Latif; İnce, Murat; Özdemir, Emrah
In this study, in order to estimate total tree height, three di?erent model structures with di?erentinput variables were produced through the use of 872 tree data points obtained from di?erentdevelopment stages and sites in coppice-originated pure sessile oak (Quercus petraea [Matt.] Liebl.)stands. These models were ?tted with machine learning techniques such as arti?cial neuralnetworks (ANNs), decision trees, support vector machines, and random forests. In addition, themodel based on DBH was ?tted and its parameters were calculated using the ordinary nonlinearleast squares method and this model was selected as the best model in Model 1. In other modelstructures, ANN model was chosen as the best estimation method based on the relative rankingmethod in which the goodness of ?t statistics of the estimation methods were evaluated together.The inclusion of stand variables in addition to the DBH measurement in the model increased theR2 by about 36% and reduced the error rate by 55%.
Economic Order Quantity: A State-of-the-Art in the Era of Uncertain Supply Chains
(2024) Alnahhal, Mohammed; Aylak, Batin Latif; Al Hazza, Muataz; Sakhrieh, Ahmad
: Inventory management is crucial for companies to minimize unnecessary costs associated with overstocking or understocking items. Utilizing the economic order quantity (EOQ) to minimize total costs is a key decision in inventory management, particularly in achieving a sustainable supply chain. The classical EOQ formula is rarely applicable in practice. For example, suppliers may enforce a minimum order quantity (MOQ) that is much larger than the EOQ. Some conditions such as imperfect quality and growing items represent variants of EOQ. Moreover, some requirements, such as the reduction of CO2 emissions, can alter the formula. Moreover, disruptions in the supply chain, such as COVID-19, can affect the formula. This study investigates which requirements must be considered during the calculation of the EOQ. Based on a literature review, 18 requirements that could alter the EOQ formula were identified. The level of coverage for these requirements has been tracked in the literature. Research gaps were presented to be investigated in future research. The analysis revealed that, despite their importance, at least 11 requirements have seldom been explored in the literature. Among these, topics such as EOQ in Industry 4.0, practical EOQ, and resilient EOQ have been identified as promising areas for future research.
Installed solar power prediction for Turkey using artificial neural network and bidirectional long short - term memory
(2020) Özdemir, Mehmet Hakan; İnce, Murat; Aylak, Batin Latif; Oral, Okan; Taş, Mehmet Ali
Sürdürülebilir bir kalkınma için yenilenebilir enerji kaynakları önemli bir rol oynamakta ve yenilenebilir enerji kaynaklı enerji üretiminin payı tüm dünyada hızla artmaktadır. Ülkemiz, bulunduğu coğrafi konumu nedeniyle hem güneş hem de rüzgâr enerjisi açısından büyük bir potansiyele sahiptir. Bu potansiyeli kullanma konusunda henüz istenen düzeye ulaşılamamıştır. Yine de son yıllarda kurulu gücün artmasıyla birlikte güneş enerjisinden elektrik üretimi çalışmaları hız kazanmıştır. Bu çalışmada, Türkiye’nin 2009-2019 yılları arasındakikümülatif güneş enerjisi kurulu gücü verileri kullanılmıştır. Bu veriler ile2020 yılı için kümülatif kurulu gücü tahmin etmekamacıylaYapay Sinir Ağı (Artificial Neural Network - ANN) ve İki Yönlü Uzun-Kısa Vadeli Bellek (Bidirectional Long Short-Term Memory - BLSTM) yöntemleri kullanılmıştır. Kümülatif kurulu güç tahmin edilmiş ve sonuçlar karşılaştırılarak yorumlanmıştır.1.INTRODUCTIONThe energy needs of countries are increasing day by day. As a result of increasing consumption, fossil energy resources in the world are rapidly running out. Nevertheless,fossil energy resources still have a considerable share in primary energy consumption acrossthe world. Primary energy consumption by sourcesin 2018 and 2019is shown forthe entire worldin Figure 1 and Figure 2. As can be seen from the Figures, the primary energy consumption originating from fossil energyresourcesis over 80% in both years.Moreover, Turkey’s primary energy consumption bysources in 2018 and 2019 isshown in Table 1.Hydroelectric energy data are not given under renewable energy in the reference.
Yapay Zeka ve Makine Öğrenmesi Tekniklerinin Lojistik Sektöründe Kullanımı
(2021) Aylak, Batin Latif; Oral, Okan; Yazıcı, Kübra
The logistics sector in Turkey and the world is growing and the sector's potential is better understood over time. It is known that the logistics sector is very open to development and has to keep up with the innovations that occur with technology. Businesses are trying to be successful in competition by keeping up with these innovations. Industry 4.0 has influenced the sectors where competition is at the forefront, especially logistics. In recent studies, it has been observed that a significant increase in the use of artificial intelligence techniques. As a result of the use of artificial intelligence in the logistics sector, changes in operations and dynamics have started to occur. Artificial intelligence models the physiological and neurological structure of human intelligence with the help of various technologies and transfers them to machines. Options such as driverless vehicles emerging with artificial intelligence, robots used in storage and shelves, and the easy use of big data in the system ensure that the errors in the logistics sector are minimized and convenience is provided in this way. Thanks to the use of artificial intelligence in the logistics sector, businesses create more efficient jobs. In this study, it is aimed to examine the artificial intelligence and machine learning applications used in the logistics industry with a broad perspective. In the study, firstly, the concepts of artificial intelligence and machine learning are explained and then, the concepts of industry and logistics are mentioned, and the applications of artificial intelligence and machine learning used in logistics are included. It is seen that artificial intelligence improves day by day and facilitates logistics processes in global logistics and supply chain management.

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