Satıç, Uğur

Profile Picture
Profile URL
https://hdl.handle.net/20.500.12573/2635
Name Variants
Satic, U.
Job Title
Dr. Öğr. Üyesi
Email Address
ugur.satic@agu.edu.tr
Main Affiliation
07.03. Endüstri Mühendisliği Anabilim Dalı
Status
Current Staff
Website
ORCID ID
0000-0002-9160-0006
Scopus Author ID
57220209733
Turkish CoHE Profile ID
370342
Google Scholar ID
WFqHep8AAAAJ
WoS Researcher ID
ABX-4934-2022
Files
5440_Uğur satıç.jpg (11.08 KB)

Sustainable Development Goals

SDG data is not available
Documents

4

Citations

63

h-index

3

Go to Scopus profile
Documents

2

Citations

52

Go to WoS profile
Scholarly Output

3

Articles

2

Views / Downloads

5/5

Supervised MSc Theses

0

Supervised PhD Theses

0

WoS Citation Count

19

Scopus Citation Count

22

WoS h-index

1

Scopus h-index

1

Patents

0

Projects

0

WoS Citations per Publication

6.33

Scopus Citations per Publication

7.33

Open Access Source

2

Supervised Theses

0

JournalCount
Bitlis Eren Üniversitesi Fen Bilimleri Dergisi1
Eurasia Proceedings of Science, Technology, Engineering and Mathematics -- 7th International Conference on Research in Engineering, Technology and Science, ICRETS 2025 -- 2025-07-10 through 2025-07-13 -- Peja -- 3439191
European Journal of Operational Research1
Current Page: 1 / 1

Scopus Quartile Distribution

Competency Cloud

GCRIS Competency Cloud

Filters

2024 - 20253
Reset filters

Settings

Scholarly Output Search Results

Now showing 1 - 3 of 3
  • Thumbnail Image
    Article
    Citation - WoS: 19
    Citation - Scopus: 22
    A Simulation-Based Approximate Dynamic Programming Approach to Dynamic and Stochastic Resource-Constrained Multi-Project Scheduling Problem
    (Elsevier, 2024) Satic, U.; Jacko, P.; Kirkbride, C.
    We consider the dynamic and stochastic resource -constrained multi -project scheduling problem which allows for the random arrival of projects and stochastic task durations. Completing projects generates rewards, which are reduced by a tardiness cost in the case of late completion. Multiple types of resource are available, and projects consume different amounts of these resources when under processing. The problem is modelled as an infinite -horizon discrete -time Markov decision process and seeks to maximise the expected discounted long -run profit. We use an approximate dynamic programming algorithm (ADP) with a linear approximation model which can be used for online decision making. Our approximation model uses project elements that are easily accessible by a decision -maker, with the model coefficients obtained offline via a combination of Monte Carlo simulation and least squares estimation. Our numerical study shows that ADP often statistically significantly outperforms the optimal reactive baseline algorithm (ORBA). In experiments on smaller problems however, both typically perform suboptimally compared to the optimal scheduler obtained by stochastic dynamic programming. ADP has an advantage over ORBA and dynamic programming in that ADP can be applied to larger problems. We also show that ADP generally produces statistically significantly higher profits than common algorithms used in practice, such as a rule -based algorithm and a reactive genetic algorithm.
  • Thumbnail Image
    Conference Object
    Minimising the Cycle Time with Assembly Line Balancing and Worker Assignment: A Case Study in a Medical Device Manufacturer Company
    (ISRES Publishing, 2025) Kayser, A.; Sipahi, G.; Sevimli, O.; Toplu, N.; Turan, S.; Satic, U.
    In this research, we considered the mixed-model assembly line balancing and worker assignment problems of a medical device manufacturer in Türkiye. We combined these problems into a single integer programming model where multiple types of products can be assembled simultaneously on a single assembly line, and workers are assigned to workstations based on their abilities while ensuring the balance and efficiency of the assembly line. Our proposed approach seeks to minimise the cycle time and ability-based assignment costs on the assembly line. We used the Gurobi solver to find the optimal solution for the proposed problem. Our approach provides higher efficiency and results in a 76% increase in productivity without requiring additional work hours or workers. © 2025 Published by ISRES Publishing.
  • Thumbnail Image
    Article
    Optimization of Warehouse Location and Inventory Management for an Industrial Textile Manufacturer Company in Türkiye
    (2024) Kaya, Rukiye; Tutkun, Tutku; Nergiz, İrem Nur; Satic, Ugur
    In this study, we consider the demand forecasting, facility location, and inventory management problems of an industrial textile manufacturer company in Türkiye. First, we begin with the demand forecasting problem for thirty-two different products and employ ABC analysis to categorise the products. Then we test multiple forecasting methods and find out that Exponential Smoothing and Croston's TSB methods perform better in our categories. Using the demand forecast results in the facility location problem, we search for a location in Europe for a warehouse. For the facility location problem, we use a mixed-integer nonlinear mathematical model to minimise the transportation cost, and warehouse rental cost. We solve the model by using GAMS Solver. Then, we handle the inventory management problem and determine the quantity of the products that are sent from the factory and the warehouse to the customer. We propose a genetic algorithm approach that generates reorder quantities and reorder points for both the factory and the warehouse to minimise the total logistics costs, including holding, ordering and stockout costs. We use simulation models to calculate the logistics costs then we use these costs as fitness values to choose the best reorder quantities and reorder points. The proposed approach offers improvement in demand forecasting, inventory management, and facility location problems and brings up a 26% reduction in total logistic costs.