Browsing by Author "Sutcu, Muhammed"

Now showing 1 - 7 of 7

Citation - WoS: 4
Citation - Scopus: 6
A 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, Muhammed
In 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.
Citation - WoS: 10
Citation - Scopus: 12
Optimizing 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. Kader
Electric 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.
Citation - Scopus: 3
Parameter Uncertainties in Evaluating Climate Policies With Dynamic Integrated Climate-Economy Model
(Springer, 2024) Sutcu, Muhammed
Climate 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. © 2024 Elsevier B.V., All rights reserved.
Citation - Scopus: 1
Electricity Load Forecasting Using Deep Learning and Novel Hybrid Models
(Sakarya University, 2022) Sutcu, Muhammed; Şahi̇n, Kübra Nur; Koloğlu, Yunus; Çelikel, Mevlüt Emirhan; Gulbahar, Ibrahim Tümay
Load 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.
Citation - WoS: 2
Citation - Scopus: 6
Probabilistic Assessment of Wind Power Plant Energy Potential Through a Copula-Deep Learning Approach in Decision Trees
(Cell Press, 2024) Sahin, Kubra Nur; Sutcu, Muhammed
In 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.
Citation - WoS: 3
Citation - Scopus: 3
Disutility Entropy in Multi-Attribute Utility Analysis
(Pergamon-Elsevier Science Ltd, 2022) Sutcu, Muhammed
In this paper, we present an alternative formulation of utility entropy called disutility entropy. Previous entropy measurements in the literature use utility density function in maximum entropy formulations. Also, in most of the cases, the sign of the cross derivative of utility functions makes it impossible to apply utility entropy for more than one attribute cases. To simplify entropy measurement and relieve some burden of this task, in this paper, we present how to use multiattribute utility functions in utility entropy formulation. We show the applicability of our proposed approach and how to apply the disutility entropy approach with given constraints to singleattribute, bi-attribute, and multiattribute utility functions. Therefore, the usefulness and feasibility of the proposed method in multiattribute utility theory field is improved. We finally discuss and interpret the application of maximum disutility entropy through several examples to illustrate the new proposed approach.
Citation - WoS: 6
Citation - Scopus: 6
Effects of Total Cost of Ownership on Automobile Purchasing Decisions
(Taylor & Francis Ltd, 2020) Sutcu, Muhammed
In this paper, we reveal a complete picture of ownership-related expenses and construct a decision model which helps decision maker to make the best choice when purchasing an automobile. The decision model helps the customers to understand what a car will cost beyond its purchase price when customers consider out-of-pocket expenses like fuel, repair, and insurance. Moreover, decision maker's preferences need to be elicited thowever, elicitation of these preferences is difficult when preferential dependencies exist or possible number of uncertainties is high. Therefore, we approximate representative joint probability distributions of a decision maker with partial information. We use a database of sedan models of all automobile brands and run a simulation to analyze the total cost of ownership of driving a car for 5 years. We found that even more expensive car could save more money over the first 5 years of ownership.