Kayışoğlu, Betül

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Profile URL
https://hdl.handle.net/20.500.12573/2665
Name Variants
Betül Kayışoğlu
Kayisoglu, Betul
Kayışoğlu, Betül
Job Title
Dr. Öğr. Üyesi
Email Address
betul.kayısoglu@agu.edu.tr
Main Affiliation
02.02. Endüstri Mühendisliği
Status
Current Staff
Website
ORCID ID
0000-0001-9979-5269
Scopus Author ID
57226412042
Turkish CoHE Profile ID
56199
Google Scholar ID
WoS Researcher ID
FDI-0613-2022
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5121_Betül Kayışoğlu.jpeg (7.68 KB)

Sustainable Development Goals

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INDUSTRY, INNOVATION AND INFRASTRUCTURE
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RESPONSIBLE CONSUMPTION AND PRODUCTION
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8

Scopus Citation Count

16

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Eurasia Proceedings of Science, Technology, Engineering and Mathematics2
Computers & Operations Research1
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  • Thumbnail Image
    Conference Object
    Citation - Scopus: 1
    Incorporating Worker Heterogeneity in Flexible Flow Shop Environment
    (ISRES Publishing, 2025) Ozpacaci, Kubra; Bekli, Seyma; Kayisoglu, Betul
    We study the flexible flow shop scheduling problem with the heterogeneous worker assignment. In many real-life manufacturing systems with flow shop environments, one of the fundamental scheduling challenges that needs to be addressed is job sequences across multiple workers. In addition, the manufacturing system may require workers to have different skills at various stages during their assignment. Therefore, worker availability at each stage may vary during the scheduling horizon. Unlike traditional flexible flow shop scheduling problem, where homogeneous workers are assumed, we consider workers with different skill levels, capabilities, and capacities. We present a mixed integer linear programming model to find the optimal sequence of job assignments, guaranteeing that jobs follow their predefined operation sequence while assigning workers with various skill sets in a flexible flow shop environment. The proposed model is tested at a battery manufacturing company. By analyzing the solution, we confirm its capability to represent the problem accurately. The proposed model offers a systematic scheduling approach for a flexible flow shop environment with a heterogeneous workforce and can be implemented in other industries. © 2025 Elsevier B.V., All rights reserved.
  • Thumbnail Image
    Article
    Citation - WoS: 8
    Citation - Scopus: 13
    Multiple Allocation Tree of Hubs Location Problem for Non-Complete Networks
    (Pergamon-Elsevier Science Ltd, 2021) Kayisoglu, Betul; Akgun, Ibrahim
    We study the Multiple Allocation Tree of Hubs Location Problem where a tree topology is required among the hubs and transportation cost of sending flows between OD pairs is minimized. Unlike most studies in the literature that assume a complete network with costs satisfying the triangle inequality to formulate the problem, we define the problem on non-complete networks and develop a modeling approach that does not require any specific cost and network structure. The proposed approach may provide more flexibility in modeling several characteristics of real-life hub networks. Moreover, the approach may produce better solutions than the classical approach, which may result from the differences in the selected hubs, the flow routes between origin-destination points, and the assignment of non-hub nodes to hub nodes. We solve the proposed model using CPLEX-based branch-and-bound algorithm and Gurobi-based branch-and-bound algorithm with Norel heuristic and develop Benders decomposition-based heuristic algorithms using two acceleration strategies, namely, strong cut generation and cut disaggregation. We conduct computational experiments using problem instances defined on non-complete networks with up to 500 nodes. The results indicate that the Benders-type heuristics are especially effective in finding good feasible solutions for large instances.
  • Thumbnail Image
    Doctoral Thesis
    Ağaç Yapılı ve Ayrıt Kapasiteli Hub Yerleşim Problemleri için Model ve Çözüm Metodolojilerinin Geliştirilmesi
    (Abdullah Gül Üniversitesi Fen Bilimleri Enstitüsü, 2022) Kayışoğlu, Betül; Kayışoğlu, Betül; Akgün, İbrahim
    In this dissertation, we study two different extensions to hub location problems, namely, Multiple Allocation Tree of Hubs Location Problem (MATHLP) that result from incorporating a tree topology requirement for the hub network and Multiple Allocation Arc Capacitated Hub Location Problem (MACHLP) that result from imposing capacities on the arcs. We consider both problems in a multiple allocation framework and try to minimize total flow cost by locating p hubs. Unlike most studies in the literature that use complete networks with costs satisfying the triangle inequality to formulate the problems, we define the problems on non-complete networks and develop a modeling approach that does not require any specific cost and network structure. Our proposed approach provides more flexibility in modeling several characteristics of real-life hub networks. We solve the proposed models using CPLEX-based algorithm and Gurobi-based algorithm with NoRel heuristic. For MATHLP, we develop Benders decomposition-based heuristic algorithms and for MACHLP, we develop a heuristic algorithm based on simulated annealing. We conduct computational experiments using problem instances defined on non-complete networks with up to 500 and 400 nodes for MATHLP and MACHLP respectively. The results indicate that the proposed solution methodologies are especially effective in finding good feasible solutions for large instances. Keywords: hub location problem, tree of hubs location problem, arc capacitated hub location problem, benders-type heuristics, simulated annealing
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 2
    Parallel Machine Scheduling With Re-Entrant Jobs With Consideration of Set Up Times
    (ISRES Publishing, 2024) Kayisoglu, Betul; Bekli, Seyma; Sahin, Ayse Sena; Akyurek, Gamze Gul; Aydinli, Ruveyda; Copur, Sevda Nur; Ekinci, Tugba
    We study the identical parallel machine problem with re-entrant jobs. Re-entrant jobs require to pass through the processing line multiple times. In many real-life manufacturing systems with parallel machine environments, one of the scheduling problems that needs to be addressed is the order of jobs on each machine with re-entrant jobs. In addition, manufacturing systems may require periodic maintenance, systematic manufacturing equipment cleaning, or predetermined upper limits on the overtime. Therefore, machine availability may vary during the scheduling horizon. We propose an integer programming model to find the optimal sequence of the re-entrant jobs at parallel machines with consideration of machine availability. The model aims to reduce setup times and maximize capacity utilization by scheduling tasks with similar set up requirements consecutively. We tested the proposed model at a panel line manufacturing company located in Turkey. The order of the panels is scheduled optimally by the proposed model for 3 different instances on the identical parallel machines for the coating process. We also provided relevant information on the user interface we developed to make the proposed scheduling model usable to by the company. The proposed model and interphase offer a systematic approach to panel line planning and can also be implemented in other industries. © 2025 Elsevier B.V., All rights reserved.