Yazar "Dosdogru, Ayse Tugba" seçeneğine göre listele

Listeleniyor 1 - 15 / 15
  • [ X ]
    Öğe
    A Novel Hybrid Artificial Intelligence Based Methodology for the Inventory Routing Problem
    (Mdpi, 2019) Boru, Asli; Dosdogru, Ayse Tugba; Gocken, Mustafa; Erol, Rizvan
    In this paper, a new hybrid method including simulation optimization and artificial intelligence based simulation is created to solve the inventory routing problem (IRP) in which three different routing strategies are evaluated for uneven demand patterns including intermittent, erratic, and lumpy demand. The proposed method includes two phases. In the first phase, a nondominated sorting genetic algorithm II based simulation is employed to perform a multi-objective search for the IRP where the objectives of the method are total supply chain cost minimization and average service level maximization. In the second phase, artificial neural network based simulation is used to adjust the reorder point and order-up-to-level by forecasting the customer demand at each replenishment time. The results of the study demonstrated that the average service level is at least 98.54% in the supply chain. From this, it can be concluded that the proposed method can provide a tremendous opportunity to improve the average service level under uncertain environments. In addition, it is determined that different routing strategies can be selected for different demand patterns according to the considered performance measures.
  • [ X ]
    Öğe
    A novel hybrid artificial intelligence-based decision support framework to predict lead time
    (Taylor & Francis Ltd, 2021) Dosdogru, Ayse Tugba; Boru Ipek, Asli; Gocken, Mustafa
    Inventory and routing are the two most important elements to company's survival in supply chain environments. Hence, solution approaches of inventory routing problem (IRP) should assure adequate inventory level and also provide an efficient route. In this case, hybrid approaches can empower researchers to solve the IRP. The aim of this study is to develop a new hybrid methodology that includes two phases to provide a generic framework for IRP. In Phase I, genetic algorithm-based simulation optimisation is used to dynamically perform inventory control and routing decisions. In Phase II, artificial intelligence (AI)-based simulation in which the lead time of supply chain members is predicted is employed to extend the functionality of the method in Phase I. The proposed hybrid methodology gives insights into the cross-fertilisation of AI, simulation, and optimisation for researchers. Therefore, this integration can be applied to different supply chain problems by using similar methods.
  • [ X ]
    Öğe
    Characterizing continuous (s, S) policy with supplier selection using Simulation Optimization
    (Sage Publications Ltd, 2017) Gocken, Mustafa; Dosdogru, Ayse Tugba; Boru, Asli; Geyik, Faruk
    A real-world inventory control system, due to its nonlinear, stochastic, time-dependent nature, and the presence of complex interactions between supply chain members, can become quite challenging to optimize and requires a complex model. At this point, the Simulation Optimization (SO) model gains a better understanding of the complex and messy phenomenon of the inventory control of supply chain members. By creating SO models for Distribution Center (DC)s and Suppliers, we wish to present flexible and comprehensible research on the important decision of whether to minimize the differences between total overordering cost and total underordering cost (Model 1) or to minimize the total supply chain cost (Model 2). We also try to point out several important issues: the optimal value of the initial inventory, the reorder point, and the order-up-to level in continuous (s, S) policy for each DC and each Supplier; whether SO models can successfully integrate the supplier selection and continuous (s, S) policy for the supply chain environment; how to apply statistical analysis skills to compare these SO models with a greater level of detail. According to the cost analysis, the total supply chain cost of Model 1 is improved approximately 22% with Model 2. Also, Model 2 is the best one according to quantity-based analysis, order-based analysis, probability-based analysis, and lead-time-based analysis. Model 2 can be successfully applied for the actual situation of the supply chain inventory system and companies can obtain a remarkable amount of saving while increasing their competitive edge.
  • [ X ]
    Öğe
    Comparative study of hybrid artificial neural network methods under stationary and nonstationary data in stock market
    (John Wiley & Sons Ltd, 2019) Dosdogru, Ayse Tugba
    In this study, a new methodology is proposed to automatically determine six parameters of artificial neural network using population-based metaheuristics. We considered following three issues: What is the effect of used metaheuristic on performance? Which parameters are mostly selected? Is there a difference between the forecasting results when using stationary or nonstationary dataset that are selected according to the augmented Dickey-Fuller test statistics? Based upon results of performance measures, proposed method leads to significant opportunities to forecast stock market more effectively. We also expect proposed methodology can provide remarkable advantages for other complex, dynamic, and nonlinear forecasting problems.
  • [ X ]
    Öğe
    Comparison of harmony search derivatives for artificial neural network parameter optimisation: stock price forecasting
    (Inderscience Enterprises Ltd, 2022) Ozcalici, Mehmet; Dosdogru, Ayse Tugba; Ipek, Asli Boru; Gocken, Mustafa
    This study has been conducted on forecasting, as accurately as possible, the next day's stock price using harmony search (HS) and its variants [improved harmony search (IHS), global-best harmony search (GHS), self-adaptive harmony search (SAHS), and intelligent tuned harmony Search (ITHS) together with artificial neural network (ANN)]. The advantage of the proposed models are that the useful information in the original stock data is found by input variable selection and simultaneously the most proper number of hidden neurons in hidden layer is discovered to mitigate overfitting/underfitting problem in ANN. The results have shown that forecasts made by HS-ANN, IHS-ANN, GHS-ANN, SAHS-ANN, and ITHS-ANN demonstrate a tendency to achieve hit rates above 89%, which is considerably better than previously proposed forecasting models in literature. Hence, ANN models provide more valuable forecasting results for investors to hedge against potential risk in stock markets.
  • [ X ]
    Öğe
    Determining the factor levels for a green supply chain using response surface methodology based discrete event simulation
    (Emerald Group Publishing Ltd, 2024) Dosdogru, Ayse Tugba; Sahin, Yeliz Buruk; Gocken, Mustafa; Ipek, Asli Boru
    Purpose This study aims to optimize the levels of factors for a green supply chain (GSC) while concurrently gaining valuable insights into the dynamic interrelationships among several factors, leading to reductions in CO2 emissions and the maximization of the average service level, thereby enhancing overall supply chain performance. Design/methodology/approach Response surface methodology (RSM) is employed as a technique for multiple response optimization. This study uses a supply chain simulation model that includes decision variables related to the level of inventory control parameters and vehicle capacity. The desirability approach is adopted to achieve optimization objectives by focusing on minimizing CO2 emissions and maximizing service levels while simultaneously determining the optimum levels of considered decision variables. Findings The high R-2 values of 97.38% for CO(2 )and 97.28% for service level, along with adjusted R-2 values reasonably close to predicted values, affirm the models' capability to predict responses accurately. Key significant model terms for CO2 encompassed reorder point, order up to quantity, vehicle capacity, and their interaction effects, while service level is notably influenced by reorder point, order up to quantity, and their interaction effects. The study successfully achieved a high level of desirability value of %99.1 and the validated performance levels confirmed that the results fall within the prediction interval. Originality/value This study introduces a metamodel framework designed to optimize various design parameters for a GSC combining discrete event simulation (DES) and RSM in the form of a simulation optimization model. In contrast to the literature, the current study offers an exhaustive and in-depth analysis of the structural elements of the supply chain, particularly the inventory control parameters and vehicle capacity, which are crucial for comprehending its performance and environmental impact.
  • [ X ]
    Öğe
    Hybrid boosting algorithms and artificial neural network for wind speed prediction
    (Pergamon-Elsevier Science Ltd, 2022) Dosdogru, Ayse Tugba; Ipek, Asli Boru
    Energy sources are an important foundation for national economic growth. The future of energy sources depend on the energy controls. The reserves of fossil energy have declined significantly, and environmental pollution has increased dramatically due to excessive fossil fuel consumption. At this point, wind energy can be used as one of the key source of renewable energy. It has a remarkable importance among the low-carbon energy technologies. The primary aim of wind energy production is to reduce dependence on fossil fuels that affect environment adversely. Therefore, wind energy is analyzed to develop new energy resources. The main issue related to evaluation of the wind energy potential is wind speed prediction. Due to the high volatile and irregular nature of wind speed, wind speed prediction is difficult. To cope with complex data structure, this study presents the development of extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), and artificial neural network (ANN) within particle swarm optimization (PSO) parameter optimization for hourly wind speed prediction. To compare the proposed hybrid methods, various performance measures, the Pearson's test, and the Taylor diagram are used. The results showed that proposed hybrid methods provide reasonable prediction results for wind speed prediction. (c) 2021 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
  • [ X ]
    Öğe
    Hybridizing Extreme Learning Machine and Bio-Inspired Computing Approaches for Improved Stock Market Forecasting
    (IEEE, 2017) Gocken, Mustafa; Boru, Asli; Dosdogru, Ayse Tugba; Ozcalici, Mehmet
    Under today's economic conditions, developing more robust and realistic forecasting methods is needed to make investments more profitable and secure. However, understanding the structure of the stock markets is very difficult because of the dynamic and non-stationary data. In this context, bio-inspired computing approaches including evolutionary computation and swarm intelligence can be used to make more accurate calculations and forecasting results. This paper improved Extreme Learning Machine (ELM) using Genetic Algorithm (GA), Differential Evolution (DE) as a two evolutionary computation methods, and Particle Swarm Optimization (PSO) and Weighted Superposition Attraction (WSA) as a two swarm intelligence methods for stock market forecasting in Turkey. The results of this study show that proposed methods can be successfully used in any real-time stock market forecasting because of the noteworthy improvement in forecasting accuracy.
  • [ X ]
    Öğe
    Improving Weather Forecasting Using De-Noising with Maximal Overlap Discrete Wavelet Transform and GA Based Neuro-Fuzzy Controller
    (World Scientific Publ Co Pte Ltd, 2019) Dosdogru, Ayse Tugba
    Adaptive Neuro-Fuzzy Inference Systems (ANFIS) is one of the most important neuro-fuzzy systems. ANFIS can be successfully applied to different real-world problems. However, it is difficult to create the ANFIS structure whose parameters directly influence the solutions. Therefore, hybrid ANFIS methods are generally used to increase efficiency and adaptability. This paper used an integrated neuro-fuzzy controller that is also known as PATSOS. The main purpose of this study is to improve the performance of the PATSOS method for weather forecasting. Our proposed PATSOS method is different from the previous ones since it embeds Genetic Algorithm (GA) into the PATSOS and also de-noising with Maximal Overlap Discrete Wavelet Transform (MODWT) is used to improve the data quality. GA is employed to optimize the moving average type, moving average degree, and de-noising degree. Furthermore, epoch number, membership function type, and membership function number for the PATSOS are optimized by GA. The results obtained by the hybrid PATSOS method are presented and compared with different cities and different models. It is concluded that proposed hybrid method forecasts daily mean temperature accurately. Proposed GA based PATSOS method can also provide remarkable advantages for determining parameter values in other complex, dynamic and non-linear forecasting problems.
  • [ X ]
    Öğe
    Integrating metaheuristics and artificial neural network for weather forecasting
    (Inderscience Enterprises Ltd, 2018) Gocken, Mustafa; Boru, Ash; Dosdogru, Ayse Tugba; Ozcalici, Mehmet
    Over the years, researchers have been analysing to forecast the weather as precisely as possible in order to provide better living conditions. Nevertheless, there is no consensus on the effective weather forecasting methods and therefore, research on providing applicable and effective forecasting methods has been continued. In this study, artificial neural networks (ANNs) are integrated with two metaheuristic methods including genetic algorithm (GA) and harmony search (HS) to determine the most relevant input variables and to search the most appropriate number of hidden neurons. The proposed forecasting methods are implemented for six different cities of Turkey that are selected according to Aydeniz's climate classification. The results of the graphical analysis and performance measures show that daily mean temperature forecasting is improved by GA-ANN and HS-ANN methods due to the ability to capture the advantages of metaheuristic and ANN simultaneously.
  • [ X ]
    Öğe
    Integrating metaheuristics and Artificial Neural Networks for improved stock price prediction
    (Pergamon-Elsevier Science Ltd, 2016) Gocken, Mustafa; Ozcalici, Mehmet; Boru, Asli; Dosdogru, Ayse Tugba
    Stock market price is one of the most important indicators of a country's economic growth. That's why determining the exact movements of stock market price is considerably regarded. However, complex and uncertain behaviors of stock market make exact determination impossible and hence strong forecasting models are deeply desirable for investors' financial decision making process. This study aims at evaluating the effectiveness of using technical indicators, such as simple moving average of close price, momentum close price, etc. in Turkish stock market. To capture the relationship between the technical indicators and the stock market for the period under investigation, hybrid Artificial Neural Network (ANN) models, which consist in exploiting capabilities of Harmony Search (HS) and Genetic Algorithm (GA), are used for selecting the most relevant technical indicators. In addition, this study simultaneously searches the most appropriate number of hidden neurons in hidden layer and in this respect; proposed models mitigate well-known problem of overfitting/underfitting of ANN. The comparison for each proposed model is done in four viewpoints: loss functions, return from investment analysis, buy and hold analysis, and graphical analysis. According to the statistical and financial performance of these models, HS based ANN model is found as a dominant model for stock market forecasting. (C) 2015 Elsevier Ltd. All rights reserved.
  • [ X ]
    Öğe
    Integration of genetic algorithm and Monte Carlo to analyze the effect of routing flexibility
    (Springer London Ltd, 2015) Dosdogru, Ayse Tugba; Gocken, Mustafa; Geyik, Faruk
    Flexibility is an important task for effectively utilizing resources in a manufacturing system and responding demands rapidly. In manufacturing systems, there exist different types of flexibility levels. In this study, the stochastic flexible job shop scheduling problem is considered to measure the impact of routing flexibility on shop performance. Thus, an integrated genetic algorithm-Monte Carlo method is proposed to analyze the effect of routing flexibility. To make the problem more realistic, system parameters (processing times, operation sequences, etc.) are generated randomly via Monte Carlo. An experimental design is utilized to analyze main and interaction effects of the factors considered (i.e., number of parts, number of machines, number of operations, and flexibility levels) by using a genetic algorithm which is specifically designed for the stochastic flexible job shop scheduling problem. In developed genetic algorithm, different initial strategies which not only improve solution quality but also decrease solution time are used. Makespan is specified as the objective function to be minimized. Results are analyzed with a full factorial analysis of variance. Comprehensive discussions of results are given case by case.
  • [ X ]
    Öğe
    (R, s, S) inventory control policy and supplier selection in a two-echelon supply chain: An Optimization via Simulation approach
    (Institute of Electrical and Electronics Engineers Inc., 2016) Gocken, Mustafa; Boru, Asli; Dosdogru, Ayse Tugba; Geyik, Faruk
    Existing literature proves that Optimization via Simulation (OvS) is relatively easy to develop regardless of the complexity of the problem and provide a much more realistic solution methodology without assumption. Hence, we used OvS to determine optimal (R, s, S) policy for Distribution Center (DC)s and suppliers and to properly select the suppliers for DCs under stochastic environmental condition and lost sales system. Determining the optimal parameters, especially determining reorder point and order-up-to level, are major challenges for (R, s, S) policy and hence, their optimal values are determined by means of OvS. Also, initial inventories of DCs and suppliers are considered because the initial conditions of a simulation are crucial aspects of simulation modeling. The proposed OvS model can be helpful for managers to understand better the scope of both the problem at hand and opportunities associated with inventory management. © 2015 IEEE.
  • [ X ]
    Öğe
    (R, s, S) INVENTORY CONTROL POLICY AND SUPPLIER SELECTION IN A TWO-ECHELON SUPPLY CHAIN: AN OPTIMIZATION VIA SIMULATION APPROACH
    (IEEE, 2015) Gocken, Mustafa; Boru, Asli; Dosdogru, Ayse Tugba; Geyik, Faruk
    Existing literature proves that Optimization via Simulation (OvS) is relatively easy to develop regardless of the complexity of the problem and provide a much more realistic solution methodology without assumption. Hence, we used OvS to determine optimal (R, s, S) policy for Distribution Center (DC) s and suppliers and to properly select the suppliers for DCs under stochastic environmental condition and lost sales system. Determining the optimal parameters, especially determining reorder point and order-up-to level, are major challenges for (R, s, S) policy and hence, their optimal values are determined by means of OvS. Also, initial inventories of DCs and suppliers are considered because the initial conditions of a simulation are crucial aspects of simulation modeling. The proposed OvS model can be helpful for managers to understand better the scope of both the problem at hand and opportunities associated with inventory management.
  • [ X ]
    Öğe
    Stock price prediction using hybrid soft computing models incorporating parameter tuning and input variable selection
    (Springer London Ltd, 2019) Gocken, Mustafa; Ozcalici, Mehmet; Boru, Asli; Dosdogru, Ayse Tugba
    Over the years, high-dimensional, noisy, and time-varying natures of the stock markets are analyzed to carry out accurate prediction. Particularly, speculators and investors are understandably eager to accurately predict stock price since millions of dollars flow through the stock markets. At this point, soft computing models have empowered them to capture the data patterns and characteristics of stock markets. However, one of the open problems in soft computing models is how to systematically determine architecture of models for given applications. In this study, Harmony Search is utilized to optimize the architecture of Neural Network, Jordan Recurrent Neural Network, Extreme Learning Machine, Recurrent Extreme Learning Machine, Generalized Linear Model, Regression Tree, and Gaussian Process Regression for 1-, 2-, 3-, 5-, 7-, and 10-day-ahead stock price prediction. The experimental results show worthy findings of stock market behavior over different prediction terms and stocks. This study also helps researchers understand which prediction model performed the best and how different conditions affect the prediction accuracy of the models. Proposed hybrid models can be successfully used by speculators and investors to make the investment or to hedge against potential risk in stock markets.