Sütçü, Muhammed
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Sütçü, Muhammed
Sutcu, Muhammed
Sutcu, Muhammed
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01. Abdullah Gül University
Mühendislik Fakültesi
Endüstri Mühendisliği
Mühendislik Fakültesi
Endüstri Mühendisliği
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Former Staff
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Sustainable Development Goals
13
CLIMATE ACTION
4
Research Products
15
LIFE ON LAND
0
Research Products
8
DECENT WORK AND ECONOMIC GROWTH
2
Research Products
10
REDUCED INEQUALITIES
0
Research Products
2
ZERO HUNGER
0
Research Products
6
CLEAN WATER AND SANITATION
0
Research Products
14
LIFE BELOW WATER
1
Research Products
11
SUSTAINABLE CITIES AND COMMUNITIES
2
Research Products
16
PEACE, JUSTICE AND STRONG INSTITUTIONS
0
Research Products
5
GENDER EQUALITY
0
Research Products
9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
1
Research Products
7
AFFORDABLE AND CLEAN ENERGY
6
Research Products
4
QUALITY EDUCATION
0
Research Products
1
NO POVERTY
1
Research Products
17
PARTNERSHIPS FOR THE GOALS
4
Research Products
3
GOOD HEALTH AND WELL-BEING
1
Research Products
12
RESPONSIBLE CONSUMPTION AND PRODUCTION
0
Research Products
This researcher does not have a Scopus ID.
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Scholarly Output
20
Articles
12
Views / Downloads
1/0
Supervised MSc Theses
6
Supervised PhD Theses
2
WoS Citation Count
29
Scopus Citation Count
38
WoS h-index
3
Scopus h-index
4
Patents
0
Projects
0
WoS Citations per Publication
1.45
Scopus Citations per Publication
1.90
Open Access Source
14
Supervised Theses
8
| Journal | Count |
|---|---|
| Bitlis Eren Üniversitesi Fen Bilimleri Dergisi | 1 |
| Computers & Industrial Engineering | 1 |
| Energy Conversion and Management: X | 1 |
| Environment Systems and Decisions | 1 |
| Erciyes Üniversitesi Fen Bilimleri Enstitüsü Dergisi | 1 |
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20 results
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Now showing 1 - 10 of 20
Article Movie Recommendation Systems Based on Collaborative Filtering: A Case Study on Netflix(Erciyes Üniversitesi, 2021) Sütçü, Muhammed; Erdem, Oğuzkan; Kaya, EcemUser ratings on items like movies, songs, and shopping products are used_x000D_ by Recommendation Systems (RS) to predict user preferences for items that have_x000D_ not been rated. RS has been utilized to give suggestions to users in various domains_x000D_ and one of the applications of RS is movie recommendation. In this domain, three_x000D_ general algorithms are applied; Collaborative Filtering that provides prediction_x000D_ based on similarities among users, Content-Based Filtering that is fed from the_x000D_ relation between item-user pairs and Hybrid Filtering one which combines these_x000D_ two algorithms. In this paper, we discuss which methods are more efficient in movie_x000D_ recommendation in the framework of Collaborative Filtering. In our analysis, we use_x000D_ Netflix Prize dataset and compare well-known Collaborative Filtering methods_x000D_ which are Singular Value Decomposition, Singular Value Decomposition++, KNearest Neighbour and Co-Clustering. The error of each method is calculated by_x000D_ using Root Mean Square Error (RMSE). Finally, we conclude that K-Nearest_x000D_ Neighbour method is more successful in our dataset.Article Machine Learning and Scenario-Based Forecasting of Türkiye’s Renewable Energy Transition toward Net-Zero 2053(Elsevier Ltd, 2026) Sutcu, Muhammed; Yildiz, Baris; Sahin, Nurettin; Almomany, Abedalmuhdi; Gulbahar, Ibrahim TumayArticle Citation - WoS: 4Citation - Scopus: 6A Variant SDDP Approach for Periodic-Review Approximately Optimal Pricing of a Slow-Moving a Item in a Duopoly Under Price Protection With End-Of Return and Retail Fixed Markdown Policy(Pergamon-Elsevier Science Ltd, 2023) Yildiz, Baris; Sutcu, MuhammedIn this paper, we examine a selling environment where a manufacturer-controlled retailer and an independent retailer sell a slow-moving A item. The manufacturer offers the independent retailer a price protection contract stipulating that the manufacturer reimburses the independent retailer in case of a reduction in the wholesale price. The price set by the independent retailer is assumed to be determined by Retail Fixed Markdown (RFM) policy. The manufacturer also offers the independent retailer a special discount rate for the replenishment orders and the retailers are assumed to follow (R, S) inventory replenishment policy. The manufacturer adopts a periodic-review pricing strategy and the mean demand observed by each retailer in a given period depends on the prices. We also take the customers choosing no-purchase option into account. We employ multinomial logit (MNL) models to forecast customers' preferences based on retail prices. The retailers' market shares are esti-mated by customized choice probability functions. We propose stochastic programming models to determine the manufacturer's pricing strategy. Then, we propose a variant Stochastic Dual Dynamic Programming (SDDP) algorithm to determine the manufacturer's approximately optimal pricing strategy by getting around three curses of dimensionality. Then, we move on to the observations on the impact of four critically important contractual parameters on the price, the market shares and the expected total net profits and finally discuss some possible approaches for the selection of the best compromise values of those contractual parameters.Article Citation - WoS: 11Citation - Scopus: 13Optimizing Electric Vehicle Charging Station Location on Highways: A Decision Model for Meeting Intercity Travel Demand(MDPI, 2023) Gulbahar, Ibrahim Tumay; Sutcu, Muhammed; Almomany, Abedalmuhdi; Ibrahim, Babul Salam K. S. M. KaderElectric vehicles have emerged as one of the top environmentally friendly alternatives to traditional internal combustion engine vehicles. The development of a comprehensive charging infrastructure, particularly determining the optimal locations for charging stations, is essential for the widespread adoption of electric vehicles. Most research on this subject focuses on popular areas such as city centers, shopping centers, and airports. With numerous charging stations available, these locations typically satisfy daily charging needs in routine life. However, the availability of charging stations for intercity travel, particularly on highways, remains insufficient. In this study, a decision model has been proposed to determine the optimal placement of electric vehicle charging stations along highways. To ensure a practical approach to the location of charging stations, the projected number of electric vehicles in Turkiye over the next few years is estimated by using a novel approach and the outcomes are used as crucial input in the facility location model. An optimization technique is employed to identify the ideal locations for charging stations on national highways to meet customer demand. The proposed model selects the most appropriate locations for charging stations and the required number of chargers to be installed, ensuring that electric vehicle drivers on highways do not encounter charging problems.Doctoral Thesis Optimal Decision-Making for Operations of Smart Grids and Microgrids(2025) Şahin, Kübra Nur; Sütçü, MuhammedYenilenebilir enerji kaynaklarının artan entegrasyonu ve elektrik üretiminin merkeziyetsizleşerek dağıtık hale gelmesi, güç sistemlerinde koordinasyon ve sistem güvenilirliği açısından önemli zorlukları beraberinde getirmiştir. Bu çalışma, akıllı enerji toplulukları için, olasılıksal modelleme, merkezî optimizasyon ve uyarlanabilir kontrol yaklaşımlarını bir araya getiren çok katmanlı bir metodolojik çerçeve sunmaktadır. İlk aşamada, meteorolojik değişkenler arasındaki karmaşık doğrusal olmayan ilişkileri modelleyebilen ve rüzgâr enerjisi potansiyelini belirsizlik altında değerlendirebilen, kopula teorisi, derin öğrenme ve karar ağaçlarını birleştiren hibrit bir yöntem geliştirilmiştir. İkinci aşamada, farklı hane yapılarını içeren bir şebekede, dağıtık enerji kaynaklarının zamanlaması ve eşler arası (P2P) enerji ticaretinin optimizasyonu için Karışık Tamsayılı Doğrusal Programlama (MILP) tabanlı model tasarlanmıştır. Son aşamada ise, kural tabanlı karar verme yapısı, Derin Deterministik Politika Gradyanı (DDPG) algoritması ile geliştirilerek, gerçek zamanlı fiyatlandırma ve merkezsiz karar alma yeteneklerine sahip bir operasyonel kontrol ortamı oluşturulmuştur. Geliştirilen model, değişken sistem koşullarına uyum sağlamakta, enerji yönetimini optimize etmekte ve belirsizlik altında uzun vadeli sistem performansını artırmaktadır. Bu çalışma, enerji sistemlerinde kaynak değerlendirmesinden operasyonel kontrole uzanan; deterministik planlamayı gerçek zamanlı, öğrenen yapılarla bütünleştiren kapsamlı bir karar destek mimarisi sunmaktadır. Elde edilen bulgular, dağıtık yenilenebilir kaynakların entegrasyonunu destekleyen, esnek, dayanıklı ve sürdürülebilir enerji sistemlerinin geliştirilmesine katkı sunmaktadır.Master Thesis Sağlık için Kalkınma Yardımında Öncelikli Bölgeler: Kanıta Dayalı Bir Yaklaşım(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2021) Chbani, Zakaria; Sütçü, MuhammedSon yıllarda sağlık alanında kalkınma yardımlarının uygunluğu ve hangi kriterlere göre belirleneceği büyük ilgi görmektedir. Bu alanda yapılan çalışmalarda, kişi başına Gayri Safi Milli Hasıla (GSMH)'nın tek indikatör olarak kullanılmasından dolayı kritik sorunlar ortaya çıkmıştır. Bu konudaki en kritik eleştiri, kişi başına düşen GSMH'nin orta gelirli ülkelerin analizlerinde çok büyük sapmalara sebep olmasıdır. Bu ülkeler önemli dezavantajlara sahip olmakla birlikte en yüksek yoksulluk ve hastalık yüküne sahip ülkelerdir. Kişi başına GSMH'nın hesaba katmadığı bazı alternatif çalışmalar literatürde önerilmiştir. Bu tez, önceki çalışmaları temel alarak, kalkınma yardımlarının uygunluğu ve tahsis kararlarında kullanılan verilere dayalı bir metodoloji sunmaktadır. Bu metodoloji, sağlık durumu ölçütlerini (bir nüfusun sağlık ve hastalık düzeyini tahmin eden) ve hastalık yüküne tepki kapasitesi ölçütlerini birleştirir. Bu çalışmada Yeti Yitimine Ayarlanmış̧ Yaşam Yılı sağlık durumunun bir ölçüsü olarak kullanılmaktadır. Kapasite ölçülerini belirlemek için öncelikli olarak literatürde kullanılan ilgili göstergeleri kapsayan bir gösterge kümesi oluşturulmuştur. Bu gösterge kümesini kullanarak, öznitelik seçimi metodu ile daha küçük ve tanımlayıcı gösterge alt kümesi oluşturulmuştur. Son olarak, seçilen göstergelerin modelde kullanılması ile ülkeler için öncelik sıralamayı elde edilmiştir. Önerilen metodoloji, kişi başına GSMH ve önceki çalışmalarla karşılaştırılarak, önerilen metodolojinin üstünlükleri gösterilmiştir. Önerilen yeni hesaplama metodolojisi 'hastalık yükü yüksek ülkeleri' ve 'aşırı yoksulluk içindeki nüfusu' hedefleyen diğer birçok metodolojiden daha iyidir. Ayrıca, önerilen metodoloji önceki çalışmalarda ele alınmayan yada çözülemeyen bazı endişeleri de kapsamaktadır.Article Citation - WoS: 2Citation - Scopus: 6Probabilistic Assessment of Wind Power Plant Energy Potential Through a Copula-Deep Learning Approach in Decision Trees(Cell Press, 2024) Sahin, Kubra Nur; Sutcu, MuhammedIn the face of environmental degradation and diminished energy resources, there is an urgent need for clean, affordable, and sustainable energy solutions, which highlights the importance of wind energy. In the global transition to renewable energy sources, wind power has emerged as a key player that is in line with the Paris Agreement, the Net Zero Target by 2050, and the UN 2030 Goals, especially SDG-7. It is critical to consider the variable and intermittent nature of wind to efficiently harness wind energy and evaluate its potential. Nonetheless, since wind energy is inherently variable and intermittent, a comprehensive assessment of a prospective site's wind power generation potential is required. This analysis is crucial for stakeholders and policymakers to make well-informed decisions because it helps them assess financial risks and choose the best locations for wind power plant installations. In this study, we introduce a framework based on Copula-Deep Learning within the context of decision trees. The main objective is to enhance the assessment of the wind power potential of a site by exploiting the intricate and non-linear dependencies among meteorological variables through the fusion of copulas and deep learning techniques. An empirical study was carried out using wind power plant data from Turkey. This dataset includes hourly power output measurements as well as comprehensive meteorological data for 2021. The results show that acknowledging and addressing the non-independence of variables through innovative frameworks like the Copula-LSTM based decision tree approach can significantly improve the accuracy and reliability of wind power plant potential assessment and analysis in other real-world data scenarios. The implications of this research extend beyond wind energy to inform decision-making processes critical for a sustainable energy future.Article Analysis of Under-Five Mortality by Diseases in Countries With Different Levels of Development: a Comparative Analysis(2023) Ersöz, Nur Şebnem; Sütçü, Muhammed; Şahan, Pınar GünerObjectives: The right to health is critical for children because they are sensitive beings who are more susceptible to disease and health problems. It would be beneficial to compare child mortality rates in countries with different levels of development and to conduct studies to address them by taking into account their causes. This study aims to analyze the situation of developed, developing and least developed countries in terms of causes under-5 child mortality (U5CM) determined by World Health Organization and to identify the similarities or differences of under-five mortality. Methods: Child mortality rates per 1,000 live births between 2000 and 2017 years in between different age groups (0-27 days and 1-59 months) by causes (disease-specific) were obtained from World Health Organization for a total 15 countries including developed, developing and least developed countries. Regression analysis was performed to identify which causes have more impact on child mortality. In addition, the relationship between diseases was calculated using Euclidean distance, and diseases were clustered using k-means clustering algorithm for each country. Results: As a result of mathematical and statistical analysis, it was seen that causes of child mortality have a significant relation with the development level of country where a child was born. Conclusions: It has been observed that the causes of child mortality in countries with different levels of development vary depending on different factors such as geographical conditions, air quality population and access to medicine.Article Citation - WoS: 3Citation - Scopus: 3Parameter Uncertainties in Evaluating Climate Policies with Dynamic Integrated Climate-Economy Model(Springer Nature, 2024) Sutcu, MuhammedClimate change is a complex issue with significant scientific and socio-economic uncertainties, making it difficult to assess the effectiveness of climate policies. Dynamic Integrated Climate-Economy Models (DICE models) have been widely used to evaluate the impact of different climate policies. However, since climate change, long-term economic development, and their interactions are highly uncertain, an accurate assessment of investments in climate change mitigation requires appropriate consideration of climatic and economic uncertainties. Moreover, the results of these models are highly dependent on input parameters and assumptions, which can have significant uncertainties. To accurately assess the impact of climate policies, it is crucial to incorporate uncertainties into these models. In this paper, we explore the impact of parameter uncertainties on the evaluation of climate policies using DICE models. Our goal is to understand whether uncertainty significantly affects decision-making, particularly in global warming policy decisions. By integrating climatic and economic uncertainties into the DICE model, we seek to identify the cumulative impact of uncertainty on climate change. Overall, this paper aims to contribute to a better understanding of the challenges associated with evaluating climate policies using DICE models, and to inform the development of more effective policy measures to address the urgent challenge of climate change.Article Citation - Scopus: 1Electricity Load Forecasting Using Deep Learning and Novel Hybrid Models(Sakarya University, 2022) Sutcu, Muhammed; Şahi̇n, Kübra Nur; Koloğlu, Yunus; Çelikel, Mevlüt Emirhan; Gulbahar, Ibrahim TümayLoad forecasting is an essential task which is executed by electricity retail companies. By predicting the demand accurately, companies can prevent waste of resources and blackouts. Load forecasting directly affect the financial of the company and the stability of the Turkish Electricity Market. This study is conducted with an electricity retail company, and main focus of the study is to build accurate models for load. Datasets with novel features are preprocessed, then deep learning models are built in order to achieve high accuracy for these problems. Furthermore, a novel method for solving regression problems with classification approach (discretization) is developed for this study. In order to obtain more robust model, an ensemble model is developed and the success of individual models are evaluated in comparison to each other. © 2025 Elsevier B.V., All rights reserved.
