Browsing by Author "Demirel, Kenan Cem"

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    Master ThesisPublicationMetadata only
    An application of digital transformation : predictive maintenance scheduling
    (2021-01-18) Demirel, Kenan Cem; Albey, Erinç; Albey, Erinç; Özener, Okan Örsan; Güler, M. G.; Department of Industrial Engineering; Demirel, Kenan Cem
    Current technological developments in the industry and literature with the fourth revolution of industry make it possible to collect accurate data from production processes and develop data-based models to increase production productivity. The management decisions can be made more intelligent and automated with process tracking systems and predictive models. Predictive Maintenance (PdM) policies stand out in this area as an opportunity, especially for mass production facilities. Necessary early actions can be taken and maintenance activities can be planned by monitoring equipment conditions and estimating failure times. At this point, creating maintenance schedules for extensive facilities using predictive model outputs also emerges as another problem. Within the scope of this thesis, an end-to-end digital transformation application has been carried out as a pilot study on a mass production line of an international scale production facility. A data collection infrastructure is established to collect process data from sensors and inspection team feedback about the equipment conditions. A PdM approach is introduced to estimate indicator scores about the equipment conditions and remaining useful lifetimes (RUL) by using the collected data. As maintenance activities require long-term operations, scheduling these activities is considered as a multi-campaign scheduling problem with sequence dependent maintenance durations. A mixed-integer linear programming (MILP) model is developed and the outputs obtained from the predictive model are fed as deterministic inputs into the model to schedule maintenance activities. Open-source applications are used in the developed solutions; thus, continuous improvement and sustainability with lower costs are aimed.
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    Conference paperPublicationMetadata only
    Ensemble Learning based on Regressor Chains: A Case on Quality Prediction
    (SciTePress, 2019) Demirel, Kenan Cem; Şahin, Ahmet; Albey, Erinç; Industrial Engineering; ALBEY, Erinç; Demirel, Kenan Cem; Şahin, Ahmet
    In this study we construct a prediction model, which utilizes the production process parameters acquired from a textile machine and predicts the quality characteristics of the final yarn. Several machine learning algorithms (decision tree, multivariate adaptive regression splines and random forest) are used for prediction. An ensemble method, using the idea of regressor chains, is developed to further improve the prediction performance. Collected data is first segmented into two parts (labeled as “normal” and “unusual”) using local outlier factor method, and performance of the algorithms are tested for each segment separately. It is seen that ensemble idea proves its competence especially for the cases where the collected data is categorized as unusual. In such cases ensemble algorithm improves the prediction accuracy significantly. Copyright © 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
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    Conference paperPublicationMetadata only
    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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    Conference paperPublicationMetadata only
    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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    Conference paperPublicationMetadata only
    A web-based decision support system for quality prediction in manufacturing using ensemble of regressor chains
    (Springer, 2020) Demirel, Kenan Cem; Şahin, Ahmet; Albey, Erinç; Industrial Engineering; ALBEY, Erinç; Demirel, Kenan Cem; Şahin, Ahmet
    In this study we construct a decision support system (DSS), which utilizes the production process parameters to predict the quality characteristics of final products in two different manufacturing plants. Using the idea of regressor chains, an ensemble method is developed to attain the highest prediction accuracy. Collected data is divided into two sets, namely “normal” and “unusual”, using local outlier factor method. The prediction performance is tested separately for each set. It is seen that the ensemble idea shows its competence especially in situations, where collected data is classified as “unusual”. We tested the proposed method in two different real-life cases: textile manufacturing process and plastic injection molding process. Proposed DSS supports online decisions through live process monitoring screens and provides real time quality predictions, which help to minimize the total number of nonconforming products.