Industrial Engineering

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

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Now showing 1 - 20 of 23

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A CART-based genetic algorithm for constructing higher accuracy decision trees
(SciTePress, 2020) Ersoy, Elif; Albey, Erinç; Kayış, Enis; Industrial Engineering; Hammoudi, S.; Quix, C.; Bernardino, J.; ALBEY, Erinç; KAYIŞ, Enis; Ersoy, Elif
Decision trees are among the most popular classification methods due to ease of implementation and simple interpretation. In traditional methods like CART (classification and regression tree), ID4, C4.5; trees are constructed by myopic, greedy top-down induction strategy. In this strategy, the possible impact of future splits in the tree is not considered while determining each split in the tree. Therefore, the generated tree cannot be the optimal solution for the classification problem. In this paper, to improve the accuracy of the decision trees, we propose a genetic algorithm with a genuine chromosome structure. We also address the selection of the initial population by considering a blend of randomly generated solutions and solutions from traditional, greedy tree generation algorithms which is constructed for reduced problem instances. The performance of the proposed genetic algorithm is tested using different datasets, varying bounds on the depth of the resulting trees and using different initial population blends within the mentioned varieties. Results reveal that the performance of the proposed genetic algorithm is superior to that of CART in almost all datasets used in the analysis.
Open Access
Çevik yöntemlerde cosmic i̇şlev puanı ve hikaye puanının birlikte kullanımı
(CEUR-WS, 2017) Ertaban, C.; Gezgin, S.; Bağrıyanık, S.; Albey, Erinç; Karahoca, A.; Industrial Engineering; Turhan, Ç.; Coşkunçay, A.; Yazıcı, A.; Oğuztüzün, H.; ALBEY, Erinç
Hikaye Puanı (SP: Story Point), Scrum ve Kanban gibi çevik yöntemlerde kullanılan en yaygın metriklerden birisidir. Subjektif bir metrik olsa da kullanışlı ve basit olması nedeniyle çevik ekiplerin birikim listelerinde bulunan kullanıcı hikayelerinin uygun bölümlere ayrılmasında, maliyet tahminlemesinde ve ekiplerin hız ve kapasitelerinin hesaplanmasında yaygın bir şekilde kullanılmaktadır. Cosmic işlev puanı (CFP: Cosmic Function Point) ise yazılım işlevsel kapsam büyüklüğünün ölçümünde kullanılan ve aynı zamanda bir ISO standardı da (ISO 19761) olan objektif bir metriktir. Bu çalışmada Türkiye’nin en büyük teknoloji ve iletişim hizmetleri sağlayıcı firmalarından birinin çevik yazılım geliştirme prensiplerine göre çalışırken hem Hikaye Puanı hem de CFP metriklerini birlikte kullanım deneyimleri paylaşılmış; iki metriğin benzerlikleri ve farklılıkları irdelenmiştir. Sonuç olarak SP metriğinin kapsam boyutlandırma toplantıları sırasında kullanıcı hikayelerinin çevik mantıkla uygun kapsam büyüklüğüne bölünmesinde daha etkin bir araç olduğu, CFP’nin ise çevik ekiplerin ürettiği çıktıların miktarının ve kalitesinin zaman içindeki trendinin ölçülmesinde ve yine çevik ortamlarda dış kaynak hak edişlerinin belirlenmesinde daha başarılı sonuçlar verdiği sonucuna varılmıştır. Ek olarak CFP’nin Efor tahminlemesinde kullanılıp kullanılamayacağı yönünde bir doğrusal regresyon modeli için ön analiz yapılmış ve ilk sonuçlar paylaşılmıştır.
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A chance constraint based multi-item production planning model using simulation optimization
(IEEE, 2016) Albey, Erinç; Uzsoy, R.; Kempf, K. G.; Industrial Engineering; ALBEY, Erinç
We consider a single stage multi-item production-inventory system under stochastic demand. We had previously proposed a production planning model integrating ideas from forecast evolution and inventory theory to plan work releases into a production facility in the face of stochastic demand. However, this model is tractable only if the capacity allocations are exogenous. This paper determines the capacity allocated to each product in each period using a genetic algorithm. Computational experiments reveal that the proposed algorithm outperforms the previous approach in both total cost and service level.
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Churn prediction for mobile prepaid subscribers
(Institute for Systems and Technologies of Information, Control and Communicatio, 2017) Can, Zehra; Albey, Erinç; Industrial Engineering; ALBEY, Erinç; Can, Zehra
In telecommunication, mobile operators prefer to acquire postpaid subscribers and increase their incoming revenue based on the usage of postpaid lines. However, subscribers tend to buy and use prepaid mobile lines because of the simplicity of the usage, and due to higher control over the cost of the line compared to postpaid lines. Moreover the prepaid lines have less paper work between the operator and subscriber. The mobile subscriber can end their contract, whenever they want, without making any contact with the operator. After reaching the end of the defined period, the subscriber will disappear, which is defined as “involuntary churn”. In this work, prepaid subscribers’ behavior are defined with their RFM data and some additional features, such as usage, call center and refill transactions. We model the churn behavior using Pareto/NBD model and with two benchmark models: a logistic regression model based on RFM data, and a logistic regression model based on the additional features. Pareto/NBD model is a crucial step in calculating customer lifetime value (CLV) and aliveness of the customers. If Pareto/NBD model proves to be a valid approach, then a mobile operator can define valuable prepaid subscribers using this and decide on the actions for these customers, such as suggesting customized offers.
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A data-driven matching algorithm for ride pooling problem
(Elsevier, 2022-04) Şahin, Ahmet; Sevim, İ.; Albey, Erinç; Güler, M. G.; Industrial Engineering; ALBEY, Erinç
This paper proposes a data-driven matching algorithm for the problem of ride pooling, which is a transportation mode enabling people to share a vehicle for a trip. The problem is considered as a variant of matching problem, since it aims to find a matching between drivers and riders. Proposed algorithm is a machine learning algorithm based on rank aggregation idea, where every feature in a multi-feature dataset provides a ranking of candidate drivers and weight for each feature is learned from past data through an optimization model. Once weight learning and candidate ranking problems are considered simultaneously, resulting optimization model becomes a nonlinear bilevel optimization model, which is reformulated as a single level mixed-integer nonlinear optimization model. To demonstrate the performance of the proposed algorithm, a real-life dataset from a mobile application of a ride pooling start-up company is used and company's current approach is considered as benchmark. Results reveal that proposed algorithm correctly predicts the first choice of riders 17% to 28% better compared to the benchmark in different scenarios. Similarly, proposed algorithm offers recommendation lists in which the preferred driver is ranked 0.38 to 1.12 person closer (to the rider's actual choice) compared to the benchmark.
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A decomposition based metaheuristic approach for solving rapid needs assessment routing problem
(Elsevier, 2021-09) Mıhçıoğlu, Yurtsev; Albey, Erinç; Industrial Engineering; ALBEY, Erinç; Mıhçıoğlu, Yurtsev
This paper proposes a decomposition based tabu search algorithm for solving multi-cover routing problem in the case of rapid need assessment. Rapid needs assessment aims to evaluate impact of a disaster at different sites to determine the needs of different community groups. Since the assessment process during a disaster is time critical, the focus in this paper is given to developing a solution approach, which aims to find high quality solutions in short period of time. The proposed algorithm focuses on a three-stage decomposition, where stages involve site selection, team assignment and routing of teams. Performance of the proposed algorithm is evaluated with respect to benchmark algorithms under different instances. The results indicate that the proposed algorithm can achieve high-quality solutions expeditiously, providing better results, on the average, when compared to the best-known solution approaches in the literature.
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Economic lot sizing problem with inventory dependent demand
(Springer Nature, 2020-11) Önal, Mehmet; Albey, Erinç; Industrial Engineering; ÖNAL, Mehmet; ALBEY, Erinç
We consider an economic lot sizing problem where the demand in a period is a piecewise linear and concave function of the amount of the available inventory after production in that period. We show that the problem isNPhard even when the production capacities are time invariant, and propose a polynomial time algorithm to the case where there are no capacity restrictions on production.
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Economic lot sizing problem with tank scheduling
(Elsevier, 2023-07-01) Önal, Mehmet; van den Heuvel, W.; Dereli, Meryem Merve; Albey, Erinç; Industrial Engineering; ÖNAL, Mehmet; ALBEY, Erinç; Dereli, Meryem Merve
We introduce a multiple-item economic lot sizing problem where items are produced through the fermentation of some raw materials. Fermentation takes place in specialized tanks that have finite capacities, and duration of the fermentation process is item dependent. When fermentation starts, the tanks are not available for the duration of the fermentation process. We analyze the complexity of this problem under various assumptions on the number of items and tanks. In particular, we show that several cases of the problem are (strongly) NP-hard, and we propose polynomial time algorithms to some single item cases. In addition, we propose a quick and simple heuristic approach for one of the multiple item cases.
Open Access
Effective training methods for automatic musical genre classification
(SciTePress, 2019) Atsız, Eren; Albey, Erinç; Kayış, Enis; Industrial Engineering; Hammoudi, S.; Quix, C.; Bernardino, J.; ALBEY, Erinç; KAYIŞ, Enis
Musical genres are labels created by human and based on mutual characteristics of songs, which are also called musical features. These features are key indicators for the content of the music. Rather than predictions by human decisions, developing an automatic solution for genre classification has been a significant issue over the last decade. In order to have automatic classification for songs, different approaches have been indicated by studying various datasets and part of songs. In this paper, we suggest an alternative genre classification method based on which part of songs have to be used to have a better accuracy level. Wide range of acoustic features are obtained at the end of the analysis and discussed whether using full versions or pieces of songs is better. Both alternatives are implemented and results are compared. The best accuracy level is 55% while considering the full version of songs. Besides, additional analysis for Turkish songs is also performed. All analysis, data, and results are visualized by a dynamic dashboard system, which is created specifically for the study.
Open Access
A hierarchical approach for solving simultaneous lot sizing and scheduling problem with secondary resources
(Elsevier, 2019) Şafak, C. U.; Yılmaz, Görkem; Albey, Erinç; Industrial Engineering; ALBEY, Erinç; YILMAZ, Görkem
This study represents a decomposition heuristic approach for simultaneous lot sizing and scheduling problem for multiple product, multiple parallel machines with secondary resources. The motivation of the study comes from the real-world instance of a plastic injection plant at Vestel Electronics. The plastic injection plant requires plastic injection molds at the planner's disposal, in order to produce variations of products, by the compatible plastic injection machines. The variations on the molds and the mold changes on the machines bring out sequence dependent major and minor setups. Since each machine requires an operator, we have extended the formulation with workforce and shift planning Results show that proposed heuristic yields comparable solutions to that of exact model for small and medium size instances; and provides schedules for the large size instances, for which exact model cannot find a feasible solution in the allotted time.
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International roaming traffic optimization with call quality
(SciTePress, 2019) Şahin, Ahmet; Demirel, Kenan Cem; Albey, Erinç; Gürsun, Gonca; Industrial Engineering; ALBEY, Erinç; Şahin, Ahmet; Demirel, Kenan Cem; Gürsun, Gonca
In this study we focus on a Steering International Roaming Traffic (SIRT) problem with single service that concerns a telecommunication’s operators’ agreements with other operators in order to enable subscribers access services, without interruption, when they are out of operators’ coverage area. In these agreements, a subscriber’s call from abroad is steered to partner operator. The decision for which each call will be forwarded to the partner is based on the user’s location (country/city), price of the partner operator for that location and the service quality of partner operator. We develop an optimization model that considers agreement constraints and quality requirements while satisfying subscribers demand over a predetermined time interval. We test the performance of the proposed approach using different execution policies such as running the model once and fixing the roaming decisions over the planning interval or dynamically updating the decisions using a rolling horizon approach. We present a rigorous trade off analysis that aims to help the decision maker in assessing the relative importance of cost, quality and ease of implementation. Our results show that steering cost is decreased by approximately 25% and operator mistakes are avoided with the developed optimization model while the quality of the steered calls is kept above the base quality level.
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Load dependent lead time modelling: a robust optimization approach
(IEEE, 2018-01-04) Albey, Erinç; Yanıkoğlu, İhsan; Uzsoy, R.; Industrial Engineering; ALBEY, Erinç; YANIKOĞLU, Ihsan
Although production planning models using nonlinear CFs have shown promising results for semiconductor wafer fabrication facilities, the lack of an effective methodology for estimating the CFs is a significant obstacle to their implementation. Current practice focuses on developing point estimates using least-squares regression approaches. This paper compares the performance of a production planning model using a multi-dimensional CF and its robust counterpart under several experimental settings. As expected, as the level of uncertainty is increased, the resulting production plan deviates from the optimal solution of the deterministic model. On the other hand, production plans found using the robust counterpart are less vulnerable to parameter estimation errors.
Open Access
A Markovian approach for time series prediction for quality control
(Elsevier, 2019) Şahin, Ahmet; Sayımlar, Ayşe Dilara; Teksan, Zehra Melis; Albey, Erinç; Industrial Engineering; TEKSAN, Zehra Melis; ALBEY, Erinç; Şahin, Ahmet; Sayımlar, Ayşe Dilara
In this work we aim to predict quality levels of incoming batches of a selected product type to a white goods manufacturer from a third party supplier. We apply a Markov Model that captures the quality level of the incoming batch in order to predict the quality status of the future arrivals. The ultimate aim is to generate reliable predictions for the future incoming batches, so that the manufacturing company could warn its supplier if the predictions indicate a significant deterioration in the quality. Applied methodology is compared to several benchmark approaches and its superior performance is shown using a benchmark dataset from the literature and the dataset provided by the manufacturing company. Proposed algorithm performs better compared to benchmarks in detecting the instances with quality level falling outside the tolerances in the validation data; and proves itself as a promising approach for the company.
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A mathematical model for customer lifetime value based offer management
(Springer, 2018) Şahin, Ahmet; Can, Zehra; Albey, Erinç; Industrial Engineering; Filipe, J.; Quix, C.; Bernardino, J.; ALBEY, Erinç; Şahin, Ahmet; Can, Zehra
Customers with prepaid lines possess higher attrition risk compared to postpaid customers, since prepaid customers do not sign long-term obligatory contracts and may churn anytime. For this reason, mobile operators have to offer engaging benefits to keep prepaid subscribers with the company. Since all such offers incur additional cost, mobile operators face an optimization problem while selecting the most suitable offers for customers at risk. In this study, an offer management framework targeting prepaid customers of a telecommunication company is developed. Proposed framework chooses the most suitable offer for each customer through a mathematical model, which utilizes customer lifetime value and churn risk. Lifetime values are estimated using logistic regression and Pareto/NBD models, and several variants of these models are used to predict churn risks using a large number of customer specific features.
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Multi-dimensional clearing functions for aggregate capacity modelling in multi-stage production systems
(Informa, 2017) Albey, Erinç; Bilge, Ü.; Uzsoy, R.; Industrial Engineering; ALBEY, Erinç
Nonlinear clearing functions have been proposed in the literature as metamodels to represent the behaviour of production resources that can be embedded in optimisation models for production planning. However, most clearing functions tested to date use a single-state variable to represent aggregate system workload over all products, which performs poorly when product mix affects system throughput. Clearing functions using multiple-state variables have shown promise, but require significant computational effort to fit the functions and to solve the resulting optimisation models. This paper examines the impact of aggregation in state variables on solution time and quality in multi-item multi-stage production systems with differing degrees of manufacturing flexibility. We propose multi-dimensional clearing functions using alternative aggregations of state variables, and evaluate their performance in computational experiments. We find that at low utilisation, aggregation of state variables has little effect on system performance; multi-dimensional clearing functions outperform single-dimensional ones in general; and increasing manufacturing flexibility allows the use of aggregate clearing functions with little loss of solution quality.
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Neural network estimators for optimal tour lengths of traveling salesperson problem instances with arbitrary node distributions
(Informs, 2024) Varol, Taha; Özener, Okan Örsan; Albey, Erinç; Industrial Engineering; ÖZENER, Okan Örsan; ALBEY, Erinç; Varol, Taha
It is essential to solve complex routing problems to achieve operational efficiency in logistics. However, because of their complexity, these problems are often tackled sequentially using cluster-first, route-second frameworks. Unfortunately, such two-phase frameworks can suffer from suboptimality due to the initial phase. To address this issue, we propose leveraging information about the optimal tour lengths of potential clusters as a preliminary step, transforming the two-phase approach into a less myopic solution framework. We introduce quick and highly accurate Traveling Salesperson Problem (TSP) tour length estimators based on neural networks (NNs) to facilitate this. Our approach combines the power of NNs and theoretical knowledge in the routing domain, utilizing a novel feature set that includes node-level, instance-level, and solution-level features. This hybridization of data and domain knowledge allows us to achieve predictions with an average deviation of less than 0.7% from optimality. Unlike previous studies, we design and employ new instances replicating real-life logistics networks and morphologies. These instances possess characteristics that introduce significant computational costs, making them more challenging. To address these challenges, we develop a novel and efficient method for obtaining lower bounds and partial solutions to the TSP, which are subsequently utilized as solution-level predictors. Our computational study demonstrates a prediction error up to six times lower than the best machine learning (ML) methods on their training instances and up to 100 times lower prediction error on out-of-distribution test instances. Furthermore, we integrate our proposed ML models with metaheuristics to create an enumeration-like solution framework, enabling the improved solution of massive scale routing problems. In terms of solution time and quality, our approach significantly outperforms the state-of-the-art solver, demonstrating the potential of our features, models, and the proposed method.
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Optimal pricing and inventory strategies for fashion products under time-dependent interest rate and demand
(Elsevier, 2021-04) Akan, M.; Albey, Erinç; Güler, M. G.; Industrial Engineering; ALBEY, Erinç
In this work we consider the dynamic pricing problem of a retailer operating in a market with a single fashion item and under time-dependent interest rate. The demand is assumed to be deterministic and dependent on the price and decay with time, i.e., the market shrinks throughout the horizon. Using an optimal-control-theoretic approach, we analytically derive the optimal pricing and inventory strategy for the retailer over a finite horizon setting. We further analyze the ramifications of the optimal pricing decision for different initial inventory levels dictated by the relationship between the supplier and the retailer; and for varying market interest rates. Optimal dynamic pricing policy is a continuous function, which is almost impossible to use in practice. This is handled using approximate piece-wise constant pricing policies. The trade-off between dynamic pricing policy and approximate policies is also investigated.
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Optimizing steering of roaming traffic with a-number billing under a rolling horizon policy
(Springer, 2020) Şahin, Ahmet; Demirel, Kenan Cem; Ceyhan, Ege; Albey, Erinç; Industrial Engineering; ALBEY, Erinç; Şahin, Ahmet; Demirel, Kenan Cem; Ceyhan, Ege
In this study, we focus on single service steering international roaming traffic (SIRT) problem by considering telecommunication operators’ agreements and “a-number billing” while keeping service quality above a certain threshold. The steering decision is made considering the origin and destination of the call, total volume requirement of bilateral agreements, quality threshold and price quote of partner operators. We develop an optimization model that considers these requirements while satisfying projected demand requirements. We suggest a framework based on rolling horizon mechanism for demand forecasting and policy updating. The results show that the steering cost is decreased approximately 11% with deterministic demand and 10% with forecasted demand compared to the base cost value provided by the company. Also, the model provides approximately 26% decrease in unsatisfied committed volume in agreements.
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A robust optimization approach for production planning under exogenous planned lead times
(IEEE, 2019) Albey, Erinç; Yanıkoğlu, İhsan; Uzsoy, R.; Industrial Engineering; ALBEY, Erinç; YANIKOĞLU, Ihsan
Many production planning models applied in semiconductor manufacturing represent lead times as fixed exogenous parameters. However, in reality, lead times must be treated as realizations of released lots' cycle times, which are in fact random variables. In this paper, we present a distributionally robust release planning model that allows planned lead time probability estimates to vary over a specified ambiguity set. We evaluate the performance of non-robust and robust approaches using a simulation model of a scaled-down wafer fabrication facility. We examine the effect of increasing uncertainty in the estimated lead time parameters on the objective function value and compare the worst-case, average optimality, and feasibility of the two approaches. The numerical results show that the average objective function value of the robust solutions are better than that of the nominal solution by a margin of almost 20% in the scenario with the highest uncertainty level.
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Robust parameter design and optimization for quality engineering
(Springer, 2022-03) Yanıkoğlu, İhsan; Albey, Erinç; Okçuoğlu, S.; Industrial Engineering; YANIKOĞLU, Ihsan; ALBEY, Erinç
This paper proposes a methodology to determine the optimal settings of key decision variables that affect the resilience of an engineering design against uncertainty. Uncertainty in quality engineering is often caused by environmental factors, and scarcity of data due to limitations in the experimentation phase amplifies the level of ambiguity. The proposed robust parameter design and optimization approach utilizes the Taguchi method to find critical variables to be used in the optimization, and it utilizes robust optimization to immunize the obtained solution against uncertainty. To demonstrate our approach, we focus on design optimization of an injection molding product, a refrigerator door cap, made from thermoplastic raw material and its key quality characteristic, warpage. The near-optimal designs found by the robust parameter design and optimization approach are implemented in a real-life manufacturing environment. The numerical experiments show that the new designs significantly improve the warpage quality characteristic and the total production cycle time compared to the current design used in the manufacturing company.

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