Yılmaz, Görkem

Profile Picture
Profile URL
https://hdl.handle.net/20.500.14365/7541
Name Variants
Yilmaz, Gorkem
Yilmaz, Goerkem
Job Title
Email Address
gorkem.yilmaz@ieu.edu.tr
gorkem.yilmaz@izmirekonomi.edu.tr
Main Affiliation
05.09. Industrial Engineering
Status
Current Staff
Website
ORCID ID
0000-0001-8845-4389
Scopus Author ID
57214665651
Turkish CoHE Profile ID
DE88A13689CA8ABC
Google Scholar ID
qPVyBVoAAAAJ
WoS Researcher ID
AAC-4411-2020
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GorkemYilmaz.jpg (11.25 KB)

Sustainable Development Goals

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Documents

5

Citations

12

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3

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Documents

9

Citations

15

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Scholarly Output

3

Articles

2

Views / Downloads

34/741

Supervised MSc Theses

0

Supervised PhD Theses

0

WoS Citation Count

6

Scopus Citation Count

5

WoS h-index

1

Scopus h-index

1

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0

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0

WoS Citations per Publication

2.00

Scopus Citations per Publication

1.67

Open Access Source

1

Supervised Theses

0

JournalCount
11th IFAC Conference on Manufacturing Modelling, Management and Control (MIM) -- Jun 30-Jul 03, 2025 -- Trondheim, Norway1
Annals of Operations Research1
Computers & Operations Research1
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Now showing 1 - 3 of 3
  • Thumbnail Image
    Article
    A Column Generation Heuristic for Simultaneous Lot-Sizing and Scheduling Problems With Secondary Resources and Setup Carryovers
    (Pergamon-elsevier Science Ltd, 2025) Safak, Cevdet Utku; Albey, Erinc; Yilmaz, Gorkem
    This study introduces an innovative approach to address the Capacitated Lot-Sizing and Scheduling Problem with Sequence-Dependent Setups (CLSD), considering both the sequence-dependent setups and costs. Facing the challenge of large-scale instances, a Column Generation-based Neighbourhood Search (CGNS) algorithm is proposed, efficiently handling real-life CLSD scenarios with extensions like secondary resources and setup carryover and crossovers. The algorithm demonstrates superior performance compared to commercial solvers and fix and relax-based benchmark algorithms, producing high-quality solutions within specified time limits on large data sets. The study's contributions include a distinctive pattern and column structure in the proposed formulation, effectively managing the exponential increase in decision variables. Test instances and a real- life case study validate the algorithm's applicability to production systems under the CLSD and Capacitated Lot-Sizing Problem (CLSP) frameworks, making it a valuable tool for optimising simultaneous lot-sizing and scheduling challenges in practical settings.
  • Thumbnail Image
    Conference Object
    Flexible Production Planning MILP Model Including Shift and Overtime Decisions
    (Elsevier, 2025) Ozel, Oyku; Yilmaz, Gorkem
    This study introduces a production planning model designed to address the complexities of shift and overtime scheduling while minimizing frequent changes in production settings over short intervals. By incorporating flexible shift and overtime scheduling, the model enables companies to efficiently manage production, inventory, and backlogs while remaining responsive to fluctuating customer demands. The proposed mixed-integer linear programming model (MILP) optimizes production, inventory, and backorder costs through product-production line allocation, shift, and overtime decisions. The objective function minimizes total costs, including fixed and variable production costs, inventory holding costs, backorder costs, shift and overtime transition costs, and idle capacity penalties. Computational experiments validate the effectiveness of the model, demonstrating its ability to improve production efficiency, reduce operational costs, and adapt to dynamic demand conditions. The findings highlight the potential of the proposed approach to support efficient and flexible production planning in dynamic manufacturing environments. Copyright (C) 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
  • Thumbnail Image
    Article
    Citation - WoS: 6
    Citation - Scopus: 5
    Multi-Stage Scenario-Based Stochastic Programming for Managing Lot Sizing and Workforce Scheduling at Vestel
    (Springer, 2023) Seyfi, S.A.; Yanıkoğlu, İ.; Yılmaz, Görkem
    This study proposes a multi-stage stochastic production planning approach for a joint lot sizing and workforce scheduling problem under demand uncertainty. Scenario trees are used to model uncertainty in demand, and a multi-stage scenario-based stochastic linear program is developed. This model allows for both here-and-now and wait-and-see decisions providing flexibility for decision-makers to adjust production quantities according to the realized portion of demand and improve the overall effectiveness of production planning by better managing the number of active lines, workforce, and inventory levels. A matheuristic is developed for large-sized instances, which yields near-optimal solutions in practicable computation times. The proposed methods are demonstrated over a real data set taken from a Turkish home and professional appliances company, Vestel. The results show significant improvements in cost and CPU time performances for benchmark approaches, verifying the effectiveness of the proposed method. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.