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Çatmakaş, Hale

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Akkocaoğlu Çatmakaş, Hale
Catmakas, Hale Akkocaoglu
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Öğr. Gör.
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06.04. Endüstri Mühendisliği
Endüstri Mühendisliği
06. Mühendislik Fakültesi
01. Çankaya Üniversitesi
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Former Staff
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Scholarly Output

2

Articles

1

Views / Downloads

763/10

Supervised MSc Theses

1

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WoS Citation Count

4

Scopus Citation Count

10

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1

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1

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WoS Citations per Publication

2.00

Scopus Citations per Publication

5.00

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1

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International Journal of Industrial Engineering Computations1
Current Page: 1 / 1

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Now showing 1 - 2 of 2
  • Master Thesis
    A new customer order scheduling problem on a single-machine with job setup times
    (2014) Akkocaoğlu Çatmakaş, Hale
    In this study, we consider a relatively new class of the customer order scheduling (COS) problem where each order consists of one or more individual jobs. All jobs in the same customer order are processed successively and delivered at the same time to the customer. Thus, the completion time of the last job processed in each customer order defines the completion time of the order. A sequence independent setup is required before the processing of each job in a customer order. However, no setup is necessary before the processing of the first job of a customer order if this first job is the same as the last job of the immediately preceding customer order. We investigate the single-machine problem for two cases in which the makespan, which is the time to complete all customer orders, is minimized in the first case while the total completion time, which is the sum of the completion time of the orders, is minimized in the second case. For some special cases of both problems, we derive the properties of the optimal solution, which can be obtained by priority rules. We show that the makespan problem is polynomially solvable. For the total completion time problem, we develop a mixed integer programming model capable of solving small-sized problem instances optimally and propose a constructive heuristic algorithm that obtains optimal and near-optimal solutions for medium and large sized problem instances. Computational experiments are done to evaluate the performance of our solution approaches in terms of both quality and time. The results show that the mixed integer linear programming model does not seem to be a useful alternative, especially for large-sized problem instances. However, the proposed heuristic algorithms find near-optimal solutions in very short time
  • Article
    Citation - WoS: 4
    Citation - Scopus: 10
    Customer Order Scheduling With Job-Based Processing on a Single-Machine To Minimize the Total Completion Time
    (Growing Science, 2021) Yeloglu, Pinar; Catmakas, Hale Akkocaoglu; Cetinkaya, Ferda Can
    This study considers a customer order scheduling (COS) problem in which each customer requests a variety of products (jobs) processed on a single flexible machine, such as the computer numerical control (CNC) machine. A sequence-independent setup for the machine is needed before processing each product. All products in a customer order are delivered to the customer when they are processed. The product ordered by a customer and completed as the last product in the order defines the customer order's completion time. We aim to find the optimal schedule of the customer orders and the products to minimize the customer orders' total completion time. We have studied this customer order scheduling problem with a job-based processing approach in which the same products from different customer orders form a product lot and are processed successively without being intermingled with other products. We have developed two mixed-integer linear programming models capable of solving the small and medium-sized problem instances optimally and a heuristic algorithm for large-sized problem instances. Our empirical study results show that our proposed tabu search algorithm provides optimal or near-optimal solutions in a very short time. We have also compared the job-based and order-based processing approaches for both setup and no-setup cases and observed that the job-based processing approach yields better results when jobs have setup times. (C) 2021 by the authors; licensee Growing Science, Canada