Industrial Engineering

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

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Open Access
Agile performance indicators for team performance evaluation in a corporate environment
(The ACM Digital Library, 2018) Ertaban, Cihangir; Sarıkaya, E.; Bağrıyanık, S.; Ertaban, Cihangir
Software development is a must for almost all industries including services, production, health and even construction. Being so widespread, software development industry needs metrics for especially two reasons; performance evaluation of development teams and continuous improvement. Moreover, use of metrics and measurements provides the ability to understand the problems and waste in the value stream so that they can be eliminated. This paper proposes a model of metrics -to be called as Agile Performance Indicators- which is also being developed and tested in the largest Digital Operator in Turkey.
Open Access
Calibrating artificial neural networks by global optimization
(2010-07) Pinter, Janos D.; Industrial Engineering; PINTER, Janos
An artificial neural network (ANN) is a computational model − implemented as a computer program − that is aimed at emulating the key features and operations of biological neural networks. ANNs are extensively used to model unknown or unspecified functional relationships between the input and output of a “black box” system. In order to apply such a generic procedure to actual decision problems, a key requirement isANN training to minimize the discrepancy between modeled and measured system output. In this work, we consider ANN training as a (potentially) multi-modal optimization problem. To address this issue, we introduce a global optimization (GO) framework and corresponding GO software. The practical viability of the GO based approach is illustrated by finding close numerical approximations of (one-dimensional, but non-trivial) functions.
Open Access
A capacitated mobile facility location problem with mobile demand: Recurrent service provision to en route refugees
(OpenProceedings.org, 2022) Pashapour, A.; Danış, Dilek Günneç; Salman, F. S.; Yücel, E.; Industrial Engineering; DANIŞ, Dilek Günneç
In this paper, we help humanitarian organizations provide service via mobile facilities (MFs) to migrating refugees, who attempt to cross international borders. Over a planning horizon, we aim to optimize number and routes and relocations of the MFs over a planning horizon. The problem is represented on a network where several refugee groups relocate in their predetermined paths throughout the periods. To incorporate continuity of service, each refugee group should be served at least once every fixed consecutive periods via capacitated MFs. We aim to minimize the total cost, consisting of fixed, service provision, and MF relocation costs, while ensuring the service continuity requirement. We formulate a mixed integer linear programming (MILP) model for this problem. We develop a matheuristic and an accelerated Benders decomposition algorithm as an exact solution method. The proposed model and solution methods are investigated over instances we extracted from the 2020 Honduras migration crisis.
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.
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.
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
Etki enbüyükleme problemi için ajan-bazlı modelleme yaklaşımı
(Afyon Kocatepe Üniversitesi, 2018) Danış, Dilek Günneç; Industrial Engineering; DANIŞ, Dilek Günneç
Pazara yeni girecek bir ürünün öncelikliolarak kullanımına sunulacağı kişilerin (hedef kümenin) belirlenmesi pazar payı tahmini yapmak için önemli, ancak çözülmesi zor bir problemdir. Bu makalede, bu problem için ajan-bazlı modelleme ile bir simülasyonçalışması geliştirilmiştir. Hedef kümeye seçilmiş kişilerin sosyal ağ üzerindeki önemi, ikna becerileri, diğerlerinin yeni ürün adaptasyonugibi karakteristik özelliklerinve hedef küme büyüklüğününürünün yayılması üzerindeki etkileri incelenmektedir.Bu özelliklerebağlı 12 farklı senaryoiçinçözümler değerlendirilmiştir.
Open Access
Evaluation of field visit planning heuristics during rapid needs assessment in an uncertain post-disaster environment
(Springer, 2022-12) Hakimifar, M.; Koyuncu, Burcu Balçık; Fikar, C.; Hemmelmayr, V.; Wakolbinger, T.; Industrial Engineering; KOYUNCU, Burcu Balçık
A Rapid Needs Assessment process is carried out immediately after the onset of a disaster to investigate the disaster’s impact on affected communities, usually through field visits. Reviewing practical humanitarian guidelines reveals that there is a great need for decision support for field visit planning in order to utilize resources more efficiently at the time of great need. Furthermore, in practice, there is a tendency to use simple methods, rather than advanced solution methodologies and software; this is due to the lack of available computational tools and resources on the ground, lack of experienced technical staff, and also the chaotic nature of the post-disaster environment. We present simple heuristic algorithms inspired by the general procedure explained in practical humanitarian guidelines for site selection and routing decisions of the assessment teams while planning and executing the field visits. By simple, we mean methods that can be implemented by practitioners in the field using primary resources such as a paper map of the area and accessible software (e.g., Microsoft Excel). We test the performance of proposed heuristic algorithms, within a simulation environment , which enables us to incorporate various uncertain aspects of the post-disaster environment in the field, ranging from travel time and community assessment time to accessibility of sites and availability of community groups. We assess the performance of proposed heuristics based on real-world data from the 2011 Van earthquake in Turkey. Our results show that selecting sites based on an approximate knowledge of community groups’ existence leads to significantly better results than selecting sites randomly. In addition, updating initial routes while receiving more information also positively affects the performance of the field visit plan and leads to higher coverage of community groups than an alternative strategy where inaccessible sites and unavailable community groups are simply skipped and the initial plan is followed. Uncertainties in travel time and community assessment time adversely affect the community group coverage. In general, the performance of more sophisticated methods requiring more information deteriorates more than the performance of simple methods when the level of uncertainty increases.
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.
Open Access
A machine learning approach to deal with ambiguity in the humanitarian decision-making
(Wiley, 2023-09) Grass, E.; Ortmann, J.; Koyuncu, Burcu Balçık; Rei, W.; Industrial Engineering; KOYUNCU, Burcu Balçık
One of the major challenges for humanitarian organizations in response planning is dealing with the inherent ambiguity and uncertainty in disaster situations. The available information that comes from different sources in postdisaster settings may involve missing elements and inconsistencies, which can hamper effective humanitarian decision-making. In this paper, we propose a new methodological framework based on graph clustering and stochastic optimization to support humanitarian decision-makers in analyzing the implications of divergent estimates from multiple data sources on final decisions and efficiently integrating these estimates into decision-making. To the best of our knowledge, the integration of ambiguous information into decision-making by combining a cluster machine learning method with stochastic optimization has not been done before. We illustrate the proposed approach on a realistic case study that focuses on locating shelters to serve internally displaced people (IDP) in a conflict setting, specifically, the Syrian civil war. We use the needs assessment data from two different reliable sources to estimate the shelter needs in Idleb, a district of Syria. The analysis of data provided by two assessment sources has indicated a high degree of ambiguity due to inconsistent estimates. We apply the proposed methodology to integrate divergent estimates in making shelter location decisions. The results highlight that our methodology leads to higher satisfaction of demand for shelters than other approaches such as a classical stochastic programming model. Moreover, we show that our solution integrates information coming from both sources more efficiently thereby hedging against the ambiguity more effectively. With the newly proposed methodology, the decision-maker is able to analyze the degree of ambiguity in the data and the degree of consensus between different data sources to ultimately make better decisions for delivering humanitarian aid.
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.
Open Access
A math-heuristic approach for two echelon vendor managed inventory routing problem
(IEOM Society, 2021) Aydın, Mehmet Can; Yavuz, Utku; Özver, Alp Orhan; Akçıl, Yarkın; Özmen, Çağla Deniz; Erol, Aslı; Aydın, Mehmet Can; Yavuz, Utku; Özver, Alp Orhan; Akçıl, Yarkın; Özmen, Çağla Deniz; Erol, Aslı
This paper studies a two-echelon vendor managed inventory routing problem of a honey packager company that delivers packaged products from a single facility to multiple retailers and customers. The objective is creating a supply chain which minimizes the total distribution cost while satisfying customers’ demand on time through retailers. The complex nature of the problem originates from connecting the inventory management and the routing process which makes getting an exact solution to the problem difficult. We propose a mathematical optimization model and develop a three-step clustering-based math-heuristic algorithm to solve the problem since commercial solvers fail to provide high-quality solutions within a given time limit. The performance of the algorithm is tested with randomly generated dimensions. The algorithm yields (on average) 16% improvement compared to objective value performances of the commercial solver.
Open Access
A mathematical model for equitable in-country COVID-19 vaccine allocation
(Taylor and Francis, 2022) Koyuncu, Burcu Balçık; Yücesoy, Ecem; Akça, Berna; Karakaya, Sırma; Kaplan, Asena Ayse; Baharmand, H.; Sgarbossa, F.; Industrial Engineering; KOYUNCU, Burcu Balçık; Yücesoy, Ecem; Akça, Berna; Karakaya, Sırma; Kaplan, Asena Ayse
Given the scarcity of COVID-19 vaccines, equitable (fair) allocation of limited vaccines across the main administrative units of a country (e.g. municipalities) has been an important concern for public health authorities worldwide. In this study, we address the equitable allocation of the COVID-19 vaccines inside countries by developing a novel, evidence-based mathematical model that accounts for multiple priority groups (e.g. elderly, healthcare workers), multiple vaccine types, and regional characteristics (e.g. storage capacities, infection risk levels). Our research contributes to the literature by developing and validating a model that proposes equitable vaccine allocation alternatives in a very short time by (a) minimising deviations from the so-called ‘fair coverage’ levels that are computed based on weighted pro-rata rations, and (b) imposing minimum coverage thresholds to control the allocation of vaccines to higher priority groups and regions. To describe the merits of our model, we provide several equity and effectiveness metrics, and present insights on different allocation policies. We compare our methodology with similar models in the literature and show its better performance in achieving equity. To illustrate the performance of our model in practice, we perform a comprehensive numerical study based on actual data corresponding to the early vaccination period in Turkey.
Open Access
MathOptimizer: a nonlinear optimization package for mathematica users
(2009) Kampas, F. J.; Pinter, Janos D.; Industrial Engineering; PINTER, Janos
Mathematica is an advanced software system that enables symbolic computing, numerics, program code development, model visualization and professional documentation in a unified framework. Our MathOptimizer software package serves to solve global and local optimization models developed using Mathematica. We introduce MathOptimizer’s key features and discuss its usage options that support a range of operational modes. The numerical capabilities of the package are illustrated by simple and more advanced examples, pointing towards a broad range of potential applications.
Open Access
A practical guide to robust optimization
(Elsevier, 2015-06) Gorissen, B. L.; Yanıkoğlu, İhsan; Hertog, D. den; Industrial Engineering; YANIKOĞLU, Ihsan
Robust optimization is a young and active research field that has been mainly developed in the last 15 years. Robust optimization is very useful for practice, since it is tailored to the information at hand, and it leads to computationally tractable formulations. It is therefore remarkable that real-life applications of robust optimization are still lagging behind; there is much more potential for real-life applications than has been exploited hitherto. The aim of this paper is to help practitioners to understand robust optimization and to successfully apply it in practice. We provide a brief introduction to robust optimization, and also describe important do׳s and don׳ts for using it in practice. We use many small examples to illustrate our discussions.
Open Access
A predictive multistage postdisaster damage assessment framework for drone routing
(Wiley, 2024-01) Adsanver, Birce; Göktürk, Elvin Çoban; Koyuncu, Burcu Balçık; Industrial Engineering; GÖKTÜRK, Elvin Çoban; Adsanver, Birce
This study focuses on postdisaster damage assessment operations supported by a set of drones. We propose a multistage framework, consisting of two phases applied iteratively to rapidly gather damage information within an assessment period. In the initial phase, the problem involves determining areas to be scanned by each drone and the optimal sequence for visiting these selected areas. We have adapted an electric vehicle routing formulation and devised a variable neighborhood descent heuristic for this phase. In the second phase, information collected from the scanned areas is employed to predict the damage status of the unscanned areas. We have introduced a novel, fast, and easily implementable imputation policy for this purpose. To evaluate the performance of our approach in real-life disasters, we develop a case study for the expected 7.5 magnitude earthquake in Istanbul, Turkey. Our numerical study demonstrates a significant improvement in response time and priority-based metrics.
Open Access
Robust reformulations of ambiguous chance constraints with discrete probability distributions
(Balikesir University, 2019) Yanıkoğlu, İhsan; Industrial Engineering; YANIKOĞLU, Ihsan
This paper proposes robust reformulations of ambiguous chance constraints when the underlying family of distributions is discrete and supported in a so-called ``p-box'' or ``p-ellipsoidal'' uncertainty set. Using the robust optimization paradigm, the deterministic counterparts of the ambiguous chance constraints are reformulated as mixed-integer programming problems which can be tackled by commercial solvers for moderate sized instances. For larger sized instances, we propose a safe approximation algorithm that is computationally efficient and yields high quality solutions. The associated approach and the algorithm can be easily extended to joint chance constraints, nonlinear inequalities, and dependent data without introducing additional mathematical optimization complexity to that of the original robust reformulation. In numerical experiments, we first present our approach over a toy-sized chance constrained knapsack problem. Then, we compare optimality and computational performances of the safe approximation algorithm with those of the exact and the randomized approaches for larger sized instances via Monte Carlo simulation.
Open Access
Stochastic production planning with flexible manufacturing systems and uncertain demand: A column generation-based approach
(Elsevier, 2022) Elyasi, Milad; Özener, Başak Altan; Ekici, Ali; Özener, Okan Örsan; Yanıkoğlu, İhsan; Economics; Industrial Engineering; ÖZENER, Başak Altan; EKİCİ, Ali; ÖZENER, Okan Örsan
The ongoing pandemic, namely COVID-19, has rendered widespread economic disorder. The deficiencies have delayed production at manufacturers in several industries on the supply side. The effects of disruption were more notable for industries with longer supply chains, especially reaching East Asia. Regarding the demand, sectors can be divided into three categories: i) the ones, like e-commerce companies, that experienced augmented demand; ii) the ones with a plunged demand, like what hotels and restaurants experience; iii) the companies experiencing a roller-coaster-ride business. After mitigation efforts, the economy started recovering, resulting in increased demand. However, regardless of their struggles, the companies have not fully returned to their pre-pandemic levels. One of the strategies to gain resilience in its supply chain and manage the disruptions is to employ flexible/hybrid manufacturing systems. This paper considers a flexible/hybrid manufacturing production setting with typically dedicated machinery to satisfy regular demand and a flexible manufacturing system (FMS) to handle surge demand. We model the uncertainty in demand using a scenario-based approach and allow the business to make here-and-now and wait-and-see decisions exploiting the cost-effectiveness of the standard production and responsiveness of the FMS. We propose a column generation-based algorithm as the solution approach. Our computational analysis shows that this hybrid production setting provides highly robust response to the uncertainty in demand, even with high fluctuations.

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