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DUMAN, Ekrem

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Now showing 1 - 10 of 25
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    VRP12 (vehicle routing problem with distances one and two) with side constraints
    (Elsevier, 2013-08) Ceranoglu, A. N.; Duman, Ekrem; Industrial Engineering; DUMAN, Ekrem
    The problem undertaken in this study is inspired from a real life application. Consider a vehicle routing problem where the distances between the customer locations are either one or two. We name this problem as VRP12 in an analogy for the name TSP12 used for the traveling salesman problem in the literature. Additionally, assume that, the time to visit each customer is not constant and the visiting time together with the travel time constitutes the capacity of the vehicle. Furthermore, each customer has two characteristics and any two customers having a common characteristic should not be visited at the same time. If visited, a penalty fee incurs. In this study, we give the formulation of this problem and suggest some simple but effective algorithms that can be used to solve it. The algorithms are built with the relaxation of the side constraints but their performances are evaluated with their success in satisfying them. Information on our case study is also provided.
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    Comparison of computational intelligence models on forecasting automated teller machine cash demands
    (Old City Publishing, 2020) Alkaya, A. F.; Gultekin, O. G.; Danaci, E.; Duman, Ekrem; Industrial Engineering; DUMAN, Ekrem
    We take up the problem of forecasting the amount of money to be withdrawn from automated teller machines (ATM). We compare the performances of eleven different algorithms from four different research areas on two different datasets. The exploited algorithms are fuzzy time series, multiple linear regression, artificial neural network, autoregressive integrated moving average, gaussian process regression, support vector regression, long-short term memory, simultaneous perturbation stochastic approximation, migrating birds optimization, differential evolution, and particle swarm optimization. The first dataset is very volatile and is obtained from a Turkish bank whereas the more stationary second dataset is obtained from a UK bank which was used in competitions previously. We use mean absolute deviation (MAD) to compare the algorithms since it provides a universal comparison ability independent of the magnitude of the data. The results show that support vector regression (SVR) performs the best on both data sets with a very short run time.
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    Migrating birds optimization: a new metaheuristic approach and its performance on quadratic assignment problem
    (Elsevier, 2012-12-25) Duman, Ekrem; Uysal, M.; Alkaya, A. F.; Industrial Engineering; DUMAN, Ekrem
    We propose a new nature inspired metaheuristic approach based on the V flight formation of the migrating birds which is proven to be an effective formation in energy saving. Its performance is tested on quadratic assignment problem instances arising from a real life problem and very good results are obtained. The quality of the solutions we report are better than simulated annealing, tabu search, genetic algorithm, scatter search, particle swarm optimization, differential evolution and guided evolutionary simulated annealing approaches. The proposed method is also tested on a number of benchmark problems obtained from the QAPLIB and in most cases it was able to obtain the best known solutions. These results indicate that our new metaheuristic approach could be an important player in metaheuristic based optimization.
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    Social media analytical CRM: a case study in a bank
    (IOS Press, 2023) Duman, Ekrem; Industrial Engineering; DUMAN, Ekrem
    The use of the social media (SM) has become more and more widespread during the last two decades, the companies started looking for insights for how they can improve their businesses using the information accumulating therein. In this regard, it is possible to distinguish between two lines of research: those based on anonymous data and those based on customer specific data. Although obtaining customer specific SM data is a challenging task, analysis of such individual data can result in very useful insights. In this study we take up this path for the customers of a bank, analyze their tweets and develop three kinds of analytical models: clustering, sentiment analysis and product propensity. For the latter one, we also develop a version where, besides the text information, the structural information available in the bank databases are also used in the models. The result of the study is a considerably more efficient set of analytical CRM models.
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    A novel collection optimisation solution maximising long-term profits: a case study in an international bank
    (Taylor & Francis, 2017-10-02) Duman, Ekrem; Ecevit, F.; Çakır, Ç.; Altan, O.; Industrial Engineering; DUMAN, Ekrem
    When customers fail to pay the amount they owe to their bank related with a credit product (credit cards, overdraft accounts or instalment loans), the bank starts the collection process. This process typically lasts for a particular amount of time after which the customer is labelled as defaulted and a litigation period is launched. Banks try to minimise the percentage of credit exposure that goes to litigation since it negatively affects the bank’s profitability. Accordingly, they take various actions to maximise the collections before litigation. In this study, we approach this problem from a different perspective as we maximise not the short-term collections but the long-term revenues from customers by incorporating the churn effects of these actions in our modelling. © 2018 Informa UK Limited, trading as Taylor & Francis Group.
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    ArticlePublicationOpen Access
    Deep reinforcement learning approach for trading automation in the stock market
    (IEEE, 2022) Kabbani, Taylan; Duman, Ekrem; Industrial Engineering; DUMAN, Ekrem; Kabbani, Taylan
    Deep Reinforcement Learning (DRL) algorithms can scale to previously intractable problems. The automation of profit generation in the stock market is possible using DRL, by combining the financial assets price 'prediction' step and the 'allocation' step of the portfolio in one unified process to produce fully autonomous systems capable of interacting with their environment to make optimal decisions through trial and error. This work represents a DRL model to generate profitable trades in the stock market, effectively overcoming the limitations of supervised learning approaches. We formulate the trading problem as a Partially Observed Markov Decision Process (POMDP) model, considering the constraints imposed by the stock market, such as liquidity and transaction costs. We then solve the formulated POMDP problem using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm reporting a 2.68 Sharpe Ratio on unseen data set (test data). From the point of view of stock market forecasting and the intelligent decision-making mechanism, this paper demonstrates the superiority of DRL in financial markets over other types of machine learning and proves its credibility and advantages in strategic decision-making.
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    Benchmarking regression algorithms for income prediction modeling
    (Elsevier, 2016) Kibekbaev, Azamat; Duman, Ekrem; Industrial Engineering; DUMAN, Ekrem; Kibekbaev, Azamat
    This paper aims to predict incomes of customers for banks. In this large-scale income prediction benchmarking paper, we study the performance of various state-of-the-art regression algorithms (e.g. ordinary least squares regression, beta regression, robust regression, ridge regression, MARS, ANN, LS-SVM and CART, as well as two-stage models which combine multiple techniques) applied to five real-life datasets. A total of 16 techniques are compared using 10 different performance measures such as R2, hit rate and preciseness etc. It is found that the traditional linear regression results perform comparable to more sophisticated non-linear and two-stage models.
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    Classification of hundreds of classes: A case study in a bank internal control department
    (IOS Press, 2023) Duman, Ekrem; Industrial Engineering; DUMAN, Ekrem
    The main function of the internal control department of a bank is to inspect the banking operations to see if they are performed in accordance with the regulations and bank policies. To accomplish this, they pick up a number of operations that are selected randomly or by some rule and, inspect those operations according to some predetermined check lists. If they find any discrepancies where the number of such discrepancies are in the magnitude of several hundreds, they inform the corresponding department (usually bank branches) and ask them for a correction (if it can be done) or an explanation. In this study, we take up a real-life project carried out under our supervisory where the aim was to develop a set of predictive models that would highlight which operations of the credit department are more likely to bear some problems. This multi-classification problem was very challenging since the number of classes were enormous and some class values were observed only a few times. After providing a detailed description of the problem we attacked, we describe the detailed discussions which in the end made us to develop six different models. For the modeling, we used the logistic regression algorithm as it was preferred by our partner bank. We show that these models have Gini values of 51 per cent on the average which is quite satisfactory as compared to sector practices. We also show that the average lift of the models is 3.32 if the inspectors were to inspect as many credits as the number of actual problematic credits.
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    Optimal ATM replenishment policies under demand uncertainty
    (Springer Nature, 2021-06) Ekinci, Y.; Serban, N.; Duman, Ekrem; Industrial Engineering; DUMAN, Ekrem
    The use of Automated Teller Machines (ATMs) has become increasingly popular throughout the world due to the widespread adoption of electronic financial transactions and better access to financial services in many countries. As the network of ATMs is becoming denser while the users are accessing them at a greater rate, the current financial institutions are faced with addressing inventory and replenishment optimal policies when managing a large number of ATMs. An excessive ATM replenishment will result in a large holding cost whereas an inadequate cash inventory will increase the frequency of the replenishments and the probability of stock-outs along with customer dissatisfaction. To facilitate informed decisions in ATM cash management, in this paper, we introduce an approach for optimal replenishment amounts to minimize the total costs of money holding and customer dissatisfaction by taking the replenishment costs into account including stock-outs. An important aspect of the replenishment strategy is that the future cash demands are not available at the time of planning. To account for uncertainties in unobserved future cash demands, we use prediction intervals instead of point predictions and solve the cash replenishment-planning problem using robust optimization with linear programming. We illustrate the application of the optimal ATM replenishment policy under future demand uncertainties using data consisting of daily cash withdrawals of 98 ATMs of a bank in Istanbul. We find that the optimization approach introduced in this paper results in significant reductions in costs as compared to common practice strategies.
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    Benchmarking regression algorithms for income prediction modeling
    (IEEE, 2015) Kibekbaev, Azamat; Duman, Ekrem; Industrial Engineering; DUMAN, Ekrem; Kibekbaev, Azamat
    This paper aims to predict incomes of customers for banks. In this large-scale income prediction benchmarking paper, we study the performance of various state-of-the-art regression algorithms (e.g. ordinary least squares regression, beta regression, robust regression, ridge regression, MARS, ANN, LS-SVM and CART, as well as two-stage models which combine multiple techniques) applied to five real-life datasets. A total of 16 techniques are compared using 10 different performance measures such as R2, hit rate and preciseness etc. It is found that the traditional linear regression results perform comparable to more sophisticated non-linear and two-stage models.