Industrial Engineering

Permanent URI for this collectionhttps://hdl.handle.net/10679/45

Browse

Now showing 1 - 20 of 25

Metadata only
Application of sequence-dependent traveling salesman problem in printed circuit board assembly
(IEEE, 2013-06) Alkaya, A. F.; Duman, Ekrem; Industrial Engineering; DUMAN, Ekrem
Optimization issues regarding the automated assembly of printed circuit boards attracted the interest of researchers for several decades. This is because even small gains in assembly time result in very important benefits in mass production. In this paper, the focus is on a particular placement machine type that has a rotational turret and a stationary component magazine. So far, this type of machine received little attention among the researchers. In this paper, the feeder configuration, placement sequencing, and assembly time minimization problems are formulated explicitly and completely (without simplifying assumptions) using nonlinear integer programming. In addition, the placement sequencing problem is shown to be a recently introduced new generalization of the traveling salesman problem (the sequence-dependent traveling salesman). These formulations show the complexity of the problems and the need for effective heuristic designs for solving them. We propose three heuristics that improve previously suggested solution methods and give comparable results when compared to simulated annealing that is a widely accepted good performing metaheuristic on combinatorial optimization problems. The heuristics are experimentally shown to improve previous methods significantly in assembly time that implies a huge economic benefit. The heuristics proposed could also be applied to other placement machines with similar operation principles.
Metadata only
Applying migrating birds optimization to credit card fraud detection
(Springer Science+Business Media, 2013) Elikucuk, I.; Duman, Ekrem; Industrial Engineering; DUMAN, Ekrem
We discuss how the Migrating Birds Optimization algorithm (MBO) is applied to statistical credit card fraud detection problem. MBO is a recently proposed metaheuristic algorithm which is inspired by the V flight formation of the migrating birds and it was shown to perform very well in solving a combinatorial optimization problem, namely the quadratic assignment problem. As analyzed in this study, it has a very good performance in the fraud detection problem also when compared to classical data mining and genetic algorithms. Its performance is further increased by the help of some modified neighborhood definitions and benefit mechanisms.
Metadata only
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.
Metadata only
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.
Metadata only
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.
Metadata only
Combining and solving sequence dependent traveling salesman and quadratic assignment problems in PCB assembly
(Elsevier, 2015-09-10) Alkaya, A. F.; Duman, Ekrem; Industrial Engineering; DUMAN, Ekrem
In this study we undertake the optimization of chip shooter component placement machines which became popular in assembling printed circuit boards (PCB) in recent years. A PCB is usually a rectangular plastic board on which the electrical circuit to be used in a particular electronic equipment is printed. The overall optimization of the chip shooter placement machines leads to a very complicated optimization problem which we formulate here for the first time (without any simplifying assumptions). However, it is possible to decompose the problem into placement sequencing problem and feeder configuration problem which turn out to be sequence dependent traveling salesman problem (SDTSP) and Quadratic Assignment Problem (QAP), respectively. We use simulated annealing metaheuristic approach and the heuristics developed for the SDTSP in an earlier study to solve these two problems in an iterative manner. We also attempt to solve the combined overall optimization problem by simulated annealing and artificial bee colony metaheuristics and compare their performances with the iterative approach. The results are in favor of iterative approach.
Metadata only
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.
Metadata only
A cost-sensitive decision tree approach for fraud detection
(Elsevier, 2013-11-01) Sahin, Y.; Bulkan, S.; Duman, Ekrem; Industrial Engineering; DUMAN, Ekrem
With the developments in the information technology, fraud is spreading all over the world, resulting in huge financial losses. Though fraud prevention mechanisms such as CHIP&PIN are developed for credit card systems, these mechanisms do not prevent the most common fraud types such as fraudulent credit card usages over virtual POS (Point Of Sale) terminals or mail orders so called online credit card fraud. As a result, fraud detection becomes the essential tool and probably the best way to stop such fraud types. In this study, a new cost-sensitive decision tree approach which minimizes the sum of misclassification costs while selecting the splitting attribute at each non-terminal node is developed and the performance of this approach is compared with the well-known traditional classification models on a real world credit card data set. In this approach, misclassification costs are taken as varying. The results show that this cost-sensitive decision tree algorithm outperforms the existing well-known methods on the given problem set with respect to the well-known performance metrics such as accuracy and true positive rate, but also a newly defined cost-sensitive metric specific to credit card fraud detection domain. Accordingly, financial losses due to fraudulent transactions can be decreased more by the implementation of this approach in fraud detection systems.
Open 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.
Metadata only
Detecting credit card fraud by modified Fisher discriminant analysis
(Elsevier, 01.04.2015) Mahmoudi, Nader; Duman, Ekrem; Industrial Engineering; DUMAN, Ekrem; Mahmoudi, Nader
In parallel to the increase in the number of credit card transactions, the financial losses due to fraud have also increased. Thus, the popularity of credit card fraud detection has been increased both for academicians and banks. Many supervised learning methods were introduced in credit card fraud literature some of which bears quite complex algorithms. As compared to complex algorithms which somehow over-fit the dataset they are built on, one can expect simpler algorithms may show a more robust performance on a range of datasets. Although, linear discriminant functions are less complex classifiers and can work on high-dimensional problems like credit card fraud detection, they did not receive considerable attention so far. This study investigates a linear discriminant, called Fisher Discriminant Function for the first time in credit card fraud detection problem. On the other hand, in this and some other domains, cost of false negatives is very higher than false positives and is different for each transaction. Thus, it is necessary to develop classification methods which are biased toward the most important instances. To cope for this, a Modified Fisher Discriminant Function is proposed in this study which makes the traditional function more sensitive to the important instances. This way, the profit that can be obtained from a fraud/legitimate classifier is maximized. Experimental results confirm that Modified Fisher Discriminant could eventuate more profit.
Metadata only
Intelligent classification-based methods in customer profitability modeling
(Springer International Publishing, 2015) Ekinci, Y.; Duman, Ekrem; Industrial Engineering; Kahraman, C.; Onar, S. C.; DUMAN, Ekrem
The expected profits from customers are important informations for the companies in giving acquisition/retention decisions and developing different strategies for different customer segments. Most of these decisions can be made through intelligent Customer Relationship Management (CRM) systems. We suggest embedding an intelligent Customer Profitability (CP) model in the CRM systems, in order to automatize the decisions that are based on CP values. Since one of the aims of CP analysis is to find out the most/least profitable customers, this paper proposes to evaluate the performances of the CP models based on the correct classification of customers into different profitability segments. Our study proposes predicting the segments of the customers directly with classification-based models and comparing the results with the traditional approach (value-based models) results. In this study, cost sensitive classification based models are used to predict the customer segments since misclassification of some segments are more important than others. For this aim, Classification and regression trees, Logistic regression and Chi-squared automatic interaction detector techniques are utilized. In order to compare the performance of the models, new performance measures are promoted, which are hit, capture and lift rates. It is seen that classification-based models outperform the previously used value-based models, which shows the proposed framework works out well.
Metadata only
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.
Metadata only
A new application of the traveling salesman problem: The Turkish cashier problem
(Ministry Communications & High Technologies Republic Azerbaijan, 2022) Duman, Ekrem; Industrial Engineering; DUMAN, Ekrem
We define the problem of finding a route for the cashier that minimizes the cost of transportation as the Turkish Cashier Problem (TCP). It is a special case of the well-known traveling salesman problem. To solve the TCP, we developed a heuristic algorithm, constructed a tight lower bound, and show that the heuristic algorithm performs very successfully for practical instances of the problem.
Metadata only
A novel and successful credit card fraud detection system Implemented in a Turkish Bank
(IEEE, 2013) Duman, Ekrem; Buyukkaya, A.; Elikucuk, I.; Industrial Engineering; DUMAN, Ekrem
We developed a credit card fraud detection solution for a major bank in Turkey. The study was completed in about three years and the developed system has been in use since February 2013. It had a great impact in the rule based fraud detection process used by the bank. Indeed, while eighty percent of the rules have been eliminated and the number of alerts has been reduced to half, a significant increase in fraud detection has been recorded. As of now the system can catch ninety seven percent of fraud attempts online or, nearly online. The study is interesting in both the formulation of the problem and the algorithms implemented. In fact, we noticed that the standard classification algorithms are not fully suitable for the fraud detection problem (as the cost of every individual false negative can be different from the others), and we looked for alternative methods, especially the meta-heuristics. Among them the newly introduced migrating birds optimization algorithm (MBO) turned out to be superior and was implemented. In addition, during the study a cost sensitive decision tree algorithm was developed and introduced to the literature.
Metadata only
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.
Metadata only
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.
Metadata only
Optimization of ATM cash replenishment with group-demand forecasts
(Elsevier, 2015-05-01) Ekinci, Y.; Lu, J.-C.; Duman, Ekrem; Industrial Engineering; DUMAN, Ekrem
In ATM cash replenishment banks want to use less resources (e.g., cash kept in ATMs, trucks for loading cash) for meeting fluctuated customer demands. Traditionally, forecasting procedures such as exponentially weighted moving average are applied to daily cash withdraws for individual ATMs. Then, the forecasted results are provided to optimization models for deciding the amount of cash and the trucking logistics schedules for replenishing cash to all ATMs. For some situations where individual ATM withdraws have so much variations (e.g., data collected from Istanbul ATMs) the traditional approaches do not work well. This article proposes grouping ATMs into nearby-location clusters and also optimizing the aggregates of daily cash withdraws (e.g., replenish every week instead of every day) in the forecasting process. Example studies show that this integrated forecasting and optimization procedure performs better for an objective in minimizing costs of replenishing cash, cash-interest charge and potential customer dissatisfaction.
Metadata only
Preface: 13th cologne-twente workshop on graphs and combinatorial optimization (CTW 2015)
(Elsevier, 2019-01-10) Duman, Ekrem; Alkaya, A. F.; Pickl, S.; Faigle, U.; Industrial Engineering; DUMAN, Ekrem
N/A
Metadata only
Profit-based artificial neural network (ANN) trained by migrating birds optimization: a case study in credit card fraud detection
(World Academy of Science, Engineering and Technology, 2015) Zakaryazad, Ashkan; Duman, Ekrem; Kibekbaev, Azamat; Industrial Engineering; DUMAN, Ekrem; Zakaryazad, Ashkan; Kibekbaev, Azamat
A typical classification technique ranks the instances in a data set according to the likelihood of belonging to one (positive) class. A credit card (CC) fraud detection model ranks the transactions in terms of probability of being fraud. In fact, this approach is often criticized, because firms do not care about fraud probability but about the profitability or costliness of detecting a fraudulent transaction. The key contribution in this study is to focus on the profit maximization in the model building step. The artificial neural network proposed in this study works based on profit maximization instead of minimizing the error of prediction. Moreover, some studies have shown that the back propagation algorithm, similar to other gradient–based algorithms, usually gets trapped in local optima and swarm-based algorithms are more successful in this respect. In this study, we train our profit maximization ANN using the Migrating Birds optimization (MBO) which is introduced to literature recently.
Metadata only
A profit-driven Artificial Neural Network (ANN) with applications to fraud detection and direct marketing
(Elsevier, 2016-01-26) Zakaryazad, Ashkan; Duman, Ekrem; Industrial Engineering; DUMAN, Ekrem; Zakaryazad, Ashkan
The rapid growth in data capture and computational power has led to an increasing focus on data-driven research. So far, most of the research is focused on predictive modeling using statistical optimization, while profit maximization has been given less priority. It is exactly this gap that will be addressed in this study by taking a profit-driven approach to develop a profit-driven Artificial Neural Network (ANN) classification technique. In order to do this, we have first introduced an ANN model with a new penalty function which gives variable penalties to the misclassification of instances considering their individual importance (profit of correctly classification and/or cost of misclassification) and then we have considered maximizing the total net profit. In order to generate individual penalties, we have modified the sum of squared errors (SSE) function by changing its values with respect to profit of each instance. We have implemented different versions of ANN of which five of them are new ones contributed in this study and two benchmarks from relevant literature. We appraise the effectiveness of the proposed models on two real-life data sets from fraud detection and a University of California Irvine (UCI) repository data set about bank direct marketing. For the comparison, we have considered both statistical and profit-driven performance metrics. Empirical results revealed that, although in most cases the statistical performance of new models are not better than previous ones, they turn out to be better when profit is the concern.

Browse

Browsing by Institution Author "DUMAN, Ekrem"