Karabağ, Oktay

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Profile URL
https://gcris.yasar.edu.tr/handle/123456789/12709
Name Variants
Job Title
Dr.Öğr.Üyesi
Email Address
Main Affiliation
01.01.09.03. Endüstri Mühendisliği Bölümü
Status
Former Staff
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ORCID ID
0000-0002-9068-1991
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
KFA-5419-2024

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Documents

16

Citations

133

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Scholarly Output

3

Articles

1

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0/3

Supervised MSc Theses

0

Supervised PhD Theses

0

WoS Citation Count

12

Scopus Citation Count

13

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0

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0

WoS Citations per Publication

4.00

Scopus Citations per Publication

4.33

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1

Supervised Theses

0

JournalCount
International Symposium for Production Research ISPR 20232
Reliability Engineering & System Safety1
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  • Thumbnail Image
    Conference Object
    Citation - Scopus: 2
    Inventory Management Optimization for Intermittent Demand
    (Springer Science and Business Media Deutschland GmbH, 2024) Berk Kaya; Oktay Karabağ; Fatma Ravza Çekiç; Bekir Can Torun; Aleyna Ömrüm Başay; Zeynep Eda Işıklı; Çağlar Çakır; Çekiç, Fatma Ravza; Kaya, Berk; Torun, Bekir Can; Çakır, Çağlar; Başay, Aleyna Ömrüm; Işıklı, Zeynep Eda; Karabağ, Oktay; N.M. Durakbasa , M.G. Gençyılmaz
    This report discusses inventory management and demand forecasting issues faced by a well-known electrical equipment company. The company requires a precise inventory management system with a wide range of products to handle its high production volume. The company has trouble forecasting intermittent demand patterns due to a lack of appropriate analytical methodologies. To overcome these challenges this study developed an inventory management system that integrates Newsvendor and Order Up Policy whose analytical methods are optimized with the inventory management policy. A comprehensive review of the existing literature on inventory management is undertaken to gather valuable information and best practices. This study has been developed based on the research conducted by Syntetos (2009). A mathematical model has been included to maximize order levels considering lead time and costs. In the model SBA and Croston methods are used for intermittent demand forecasting. This model includes various parameters and assumptions that allow calculating expected total costs and determining the optimum order level that efficiently meets customer demand while minimizing expenses. The methods employed optimize inventory management minimize inventory cost and enhance customer satisfaction. © 2024 Elsevier B.V. All rights reserved.
  • Thumbnail Image
    Conference Object
    Maintenance Decision and Spare Part Selection for Multi-component System
    (Springer Science and Business Media Deutschland GmbH, 2024) Berk Kaya; Oktay Karabağ; Mehmet Murat Fadiloglu; Kaya, Berk; Karabağ, Oktay; Fadıloğlu, Mehmet Murat; N.M. Durakbasa , M.G. Gençyılmaz
    Due to the advancement of technology over time higher technology machines are being used in the production and service sectors. Companies suffer great financial losses if these machines stop working due to a breakdown. To avoid these losses maintenance has become increasingly important for companies over time. Condition based maintenance aims to intervene in a system as close to the point of failure as possible using information received from the system. Sensors are used to obtain information about the wear and tear of the machine. However since sensors are costly they are not installed on every machine component but rather on the system. While this reduces costs it also means that we now obtain partial information from the system rather than from each component. In these systems we need to make two types of decisions. The first decision is when to intervene in the system. The second decision is how many spare parts to carry with us once we decide to intervene. We simulated several different experiments for a periodic system composed of identical components and found optimal policies based on the two decisions we made. Our managerial insights indicate that as the number of components in the machine increases the importance of selecting spare parts for the system also increases leading to a tendency to maintain the system as late as possible before the system fails. Moreover in situations where the penalty for maintenance is lower after a failure occurs in optimal policy we maintain later and carry more spare parts during our interventions. © 2024 Elsevier B.V. All rights reserved.
  • Thumbnail Image
    Article
    Citation - WoS: 12
    Citation - Scopus: 11
    An efficient procedure for optimal maintenance intervention in partially observable multi-component systems
    (Elsevier Ltd, 2024) Oktay Karabağ; Önder Bulut; Ayhan Özgür Toy; Mehmet Murat Fadiloglu; Toy, Ayhan Özgür; Karabağ, Oktay; Bulut, Önder; Fadıloğlu, Mehmet Murat
    With rapid advances in technology many systems are becoming more complex including ever-increasing numbers of components that are prone to failure. In most cases it may not be feasible from a technical or economic standpoint to dedicate a sensor for each individual component to gauge its wear and tear. To make sure that these systems that may require large capitals are economically maintained one should provide maintenance in a way that responds to captured sensor observations. This gives rise to condition-based maintenance in partially observable multi-component systems. In this study we propose a novel methodology to manage maintenance interventions as well as spare part quantity decisions for such systems. Our methodology is based on reducing the state space of the multi-component system and optimizing the resulting reduced-state Markov decision process via a linear programming approach. This methodology is highly scalable and capable of solving large problems that cannot be approached with the previously existing solution procedures. © 2024 Elsevier B.V. All rights reserved.