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Analyzing airport service quality through sentiment analysis using machine learning techniques
(CRC Press, 2024) Aylak, Batin Latif
The chapter highlights various machine learning models that have been employed to evaluate the effectiveness of airport services and raise customer satisfaction. The characteristics that influence traveller satisfaction and raise the standard of airport services have been identified using statistical techniques such as logistic regression, decision trees, and random forest models. Numerous studies have used decision trees to identify the factors that are most important in determining the standard of airport services and to provide recommendations for improvement based on the identified factors. Recurrent neural networks with long-term learning capabilities include those with long short-term memory. © 2025 selection and editorial matter, Turan Paksoy and Sercan Demir. All rights reserved.
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.
Application of machine learning methods for pallet loading problem
(MDPI-Multidisciplinary Digital Publishing Institute, 2021) Aylak, Batin Latif; İnce, Murat; Oral, Okan; Almasarwah, Najat; Singh, Manjeet; Salah, Bashir; Süer, Gursel
Because of continuous competition in the corporate industrial sector, numerous companies are always looking for strategies to ensure timely product delivery to survive against their competitors. For this reason, logistics play a significant role in the warehousing, shipments, and transportation of the products. Therefore, the high utilization of resources can improve the profit margins and reduce unnecessary storage or shipping costs. One significant issue in shipments is the Pallet Loading Problem (PLP) which can generally be solved by seeking to maximize the total number of boxes to be loaded on a pallet. In many previous studies, various solutions for the PLP have been suggested in the context of logistics and shipment delivery systems. In this paper, a novel two-phase approach is presented by utilizing a number of Machine Learning (ML) models to tackle the PLP. The dataset utilized in this study was obtained from the DHL supply chain system. According to the training and testing of various ML models, our results show that a very high (>85%) Pallet Utilization Volume (PUV) was obtained, and an accuracy of >89% was determined to predict an accurate loading arrangement of boxes on a suitable pallet. Furthermore, a comprehensive analysis of all the results on the basis of a comparison of several ML models is provided in order to show the efficacy of the proposed methodology.
Artificial intelligence and machine learning applications in agricultural supply chain: a critical commentary
(Fresenius Environmental Bulletin, 2021) Aylak, Batin Latif
Integration of AI and ML technologies in the agricultural supply chain (ASC) is revolutionalizing, the domain by bringing in robust monitoring and prediction as well as quick decision-making abilities. A comprehensive literature analysis of the applications of artificial intelligence methods and machine learning algorithms in the agricultural supply chain is demonstrated in this study. In order to solve complicated challenges confronted by various areas of the agricultural supply chain, this literature analysis addresses different significant works that machine learning and artificial intelligence methods are used. Different AI and ML applications were suggested for the following areas of agriculture belonging to different phases: (i) crop yield prediction, prediction of soil properties and irrigation management; (ii) weather prediction, disease detection and weed detection, (iii) demand management and production planning, (iv) transportation, storage, inventory and retailing. In order to remain unbiased and objective, different studies from different journals were analyzed for each phase. It is observed that the majority of these studies focus on crop yield and soil properties prediction. It is also inferred that artificial neural networks, support vector machines, utilization of unmanned aerial vehicles, and remote sensors are fairly popular in the agriculture discipline.
Avrupa Yeşil Mutabakatının Lojistik üzerindeki Etkilerini CIMO-Logic ile Analizi
(Osman SAĞDIÇ, 2022) Taş, Mehmet Ali; Aylak, Batin Latif
Avrupa Yeşil Mutabakatı (AYM), Avrupa'yı 2050 yılına kadar karbon-nötr haline gelen ilk kıta haline getirmeyi amaçlayan bir dizi mevzuat ve politika kılavuzudur. AYM’ye göre, sürdürülebilir kalkınma hedefine ulaşmak için çeşitli alanlarda değişiklik ve dönüşümler öngörülmektedir. Lojistik sektörünün de odak noktası, bu mutabakata uygun olarak eko-lojistik olarak da bilinen yeşil lojistiğe kaymıştır. Söz konusu kavram, depolama, nakliye ve diğer lojistik faaliyetlerin çevresel etkisini en aza indirmek için lojistik endüstrisi tarafından uygulanan sürdürülebilir yaklaşımları tanımlamaktadır. AYM'nin lojistik üzerindeki etkileri incelenmeli ve daha iyi sonuçlar verebilecek uygulamalar geliştirilmelidir. Bu çalışmada, süreçlerin incelenmesini ve bilimsel kanıt sunulmasını sağlayan tasarım bilimi araştırma yaklaşımlarından biri olan CIMO-Logic'ten yararlanılmıştır. Çalışma bulgularının, mutabakatın lojistik uygulamaları üzerindeki etkilerini ortaya koymaya yardımcı olması amaçlanmaktadır.
Data-driven interpretable ensemble learning methods for the prediction of wind turbine power incorporating SHAP analysis
(Elsevier, 2023) Çakıroğlu, Celal; Demir, Sercan; Özdemir, Mehmet Hakan; Aylak, Batin Latif; Sariisik, Gencay; Abualigah, Laith
Wind energy increasingly attracts investment from many countries as a clean and renewable energy source. Since wind energy investment cost is high, the efficiency of a potential wind power plant should be determined using wind power prediction models and wind speed data before installation. Accurate wind power estimation is crucial to set up comprehensive strategies for wind power generation. This study estimated the power produced in a wind turbine using six different regression algorithms based on machine learning using temperature, humidity, pressure, air density, and wind speed data. The proposed estimation model was evaluated on the data received between 2011 and 2020 at station 17,112 in Çanakkale, Turkey. XGBoost, Random Forest, LightGBM, CatBoost, AdaBoost, and M5-Prime algorithms were used to create predictive models. Furthermore, model explanations were presented using the SHAP methodology. Among the regression algorithms evaluated according to the R2 performance metric, the best performance was obtained from the XGBoost algorithm. Regarding computational speed, the LightGBM model emerged as the most efficient model. The wind speed was
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.
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%.
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.
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.
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.
Investigating the Effects of Digital Challenges and Strategies in Post-Pandemic Era on Supply Chain Management
(Verlag Peter Lang AG, 2022) Taş, Mehmet Ali; Aylak, Batin Latif; Uşar, Damla Durak
With the recent outbreak of the pandemic, the vulnerability of the global supply chains has been revealed. Significant disruption from both the demand and supply sides has impacted drastically the supply chain operation around the world. There is a growing trend to integrate digital supply chain technologies to make the supply resilient and robust. However, businesses from different industries can face internal and external challenges dealing with technology trends in supply chain management. This study is intended to clarify and analyze the challenges in implementing digital strategies of supply chains whose conditions are constantly changing due to the pandemic. Exploratory research is conducted through an extensive literature review to examine these challenges. The Context-Intervention-Mechanism-Outcome (CIMO-logic) framework was used to determine the digital strategies in supply chains. The analysis study indicated that businesses can face challenges starting from selecting technology and designing strategies to coordination and collaboration with the internal and external supply chain participants. According to the problems defined in the context, digital strategies were proposed with the CIMO-logic framework. The findings of this study can guide industry practitioners to address the challenges in supply chain digitalization in advance. © Peter Lang GmbH.
Investigation of blockchain technology integration within food supply chain management
(American Society for Testing and Materials, 2022) Taş, Mehmet Ali; Aylak, Batin Latif
Blockchain technologies (BT) began to be used in many areas because of the many advantages they offer. In this respect, BT applications take place in different types of supply chains. One of the areas where blockchain is used is the food supply chain (FSC). FSC is a general concept that also covers different types such as agri-FSC and cold chain. These supply chains need the ad-vantages of modern BT for crucial reasons such as the products' time-dependent nature and direct impact on human health. We evaluate the use of blockchain applications in the field of FSC in order to provide a deep understanding of problems inherent to the FSC and present insights to companies into developing and implementing their own blockchain-driven solutions to address the FSC performance challenges. As the FSC is a complex and dynamic system, where multi-stakeholders are involved, a systematic approach should be established to reveal the BT integration behavior of the FSC. An extensive literature review was conducted within the scope of the study. A critical review of the studies shows which BT applications are used in the FSC chain to drive supply chain performance, their application areas, and main problems in various aspects in the applications. Using the causal loop diagram (CLD) as a system dynamics approach, the interrelationship of causes and effects is visualized and prescriptive information is generated to guide researchers and practitioners in the field of new technology adoption in FSCs in general and BT integration in particular. This study is intended to encourage the use of BT in FSC applications.
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]
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.
Prediction of Turkey's electricity generation by sources using artifical neural network and bidirectional long short - term memory
(2021) Aylak, Batin Latif; Özdemir, Mehmet Hakan; İnce, Murat; Oral, Okan
It is an indisputable fact that energy plays a big role in the development of countries. Electrical energy has a great share in the development. Electricity is a secondary energy source, i.e. it is obtained by transforming primary energy sources. Although the desired level has not yet been reached, Turkey’s installed power has increased by years and a wide variety of energy sources such as coal, oil, natural gas, hydroelectric energy, wind, solar and other renewable energy sources are used in electricity generation. At this point, it is observed that the share of renewable energy sources in total electricity generation has increased from year to year. It should be underlined that this increase is very important for the country’s economy. In this study, Turkey’s electricity generation by sources for the years 2020 and 2021 was predicted with artificial neural network (ANN) and bidirectional long short - term memory (BLSTM) methods using the data for electricity generation by sources in the years 2010-2019. The share of electricity generated from renewable energy sources in total electricity generation for 2020 by ANN and BLSTM methods was calculated as 18.08% and 18.6% respectively. For 2021, the share of electricity generated from renewable energy sources in total electricity generation was calculated as 21.95% and 21.68% respectively. These results show that the share of electricity generated from renewable energy sources in total electricity generation will increase. Finally, suggestions were made on what kind of roadmap should be followed in the field of investments in renewable energy resources.
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).
Standortplanung einer windkraftanlage : Eine fallanalyse aus der Türkei
(2024) Özdoğar, Seda Deniz; Aylak, Batin Latif; Taş, Mehmet Ali; Van, Yunus Emre
Der Konsum hat aufgrund des Anstiegs der Weltbevölkerung und des Wirtschaftswachstums rapide zugenommen. Der Anstieg des Verbrauchs hat zu einem Anstieg des Energiebedarfs geführt. Man geht davon aus, dass diese Situation durch Lösungen wie mehr Energieproduktion und effizientere Energienutzung bewältigt werden kann. Allerdings sollten bei der Entwicklung dieser Lösungen auch die Nachhaltigkeit und Umweltauswirkungen des Energiesektors berücksichtigt werden. In diese Richtung nimmt die Nutzung der Windenergie weltweit zu. Die Türkei gehört aufgrund seiner Lage zu den am besten geeigneten Ländern des Windenergiepotenzials. Ziel dieser Studie ist es, eine mögliche Standortbestimmung für Windkraftanlagen in Tekirdağ auf der Grundlage von multikriteriellen Entscheidungskriterien unter Berücksichtigung der aktuellen Situation von Windkraftanlagen in der Türkei vorzuschlagen. Zu diesem Zweck wird die Standortbestimmung der geplanten Windkraftanlage in Tekirdağ, einer der Provinzen mit dem höchsten Potenzial in der Türkei, mit multikriteriellen Entscheidungsunterstützungs (MCDM)-Ansätzen diskutiert. Die zehn Faktoren, die bei der Windparkinstallation bewertet werden können, wurden durch Literaturrecherche und Feedback von Experten ermittelt und die Gewichte dieser Faktoren mit der AHP berechnet. Mithilfe von Gewichten und TOPSIS werden zehn Standorte in Tekirdağ im Hinblick auf die Anwendbarkeit von Windparks aufgelistet. Infolgedessen wurde der Standort 22 km vom Bezirk Şarköy entfernt als am besten geeigneter Punkt ermittelt. Ziel ist es, dass die Studie in Zukunft zu Studien zur nachhaltigen Energiebewertung beitragen wird
Tedarik Zinciri Kademesinin ISO 9001 Kalite Yönetim Sertifikasyonu ve Finansal Performans İlişkisine Etkisi: Türkiye Örneği
(2021) Aylak, Batin Latif; Uşar, Damla Durak; Kayıkcı, Yaşanur
ISO 9001 Kalite Yönetim Sistemi belgesine sahip olmak günümüzde firmalar için önem arz etmektedir. Firmaların bütün kademelerinde olduğu gibi ISO 9001 belgesine sahip olmak tedarik zinciri kademesinde de çeşitli getiriler sağlamaktadır. Bu belge ile birlikte işletmeler daha iyi yönetilirken bir yandan da işletmelerin içinde bulunan bütün birimleri ilgilendiren süreçler için sürekli bir iyileştirme sağlanmaktadır. İşletmelerin finansal performansı küresel rekabet ortamında işletmelerin başarılı olmaları için önemli bir göstergedir. Bu çalışmada BIST’e kote 165 adet üretim firmasından oluşan bir örneklem ile tedarik zinciri kademesinin, ISO 9001 Kalite Yönetim Sertifikasyonu ve finansal performans ilişkisine etkileri incelenmiştir. Firmalar tedarik zinciri kademelerine göre gruplandırılmış ve ISO 9001 belgesine sahip olmanın bu gruplardaki firmanın finansal performansına etkisi panel veri analizi yöntemi ile analiz edilmiştir. Çalışma sonucunda ISO 9001 kalite yönetim belgesine sahip olan firmaların bu belgeye sahip olmayan firmalara göre daha çok aktif karlılığa sahip oldukları tespit edilmiştir. Ancak tedarik zinciri kademesine göre bu ilişkiyi etkileyen farklı mekanizmaların olduğu saptanmıştır. Bulunan sonuçlar yöneticilere ISO 9001 belgesinin stratejik açıdan etkinliği konusunda rehberlik sağlamaktadır.

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