Yazar "Allahverdi, Muberra" seçeneğine göre listele

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    Öğe
    A Substantially Improved New Algorithm for Flowshop Scheduling Problem with Uncertain Processing Times
    (Kırıkkale Üniversitesi, 2022) Allahverdi, Muberra
    The performance measure of total completion time (TCT) plays a key role in manufacturing to improve performance, e.g., reducing inventory levels. Moreover, since uncertainty is an inevitable part of certain manufacturing environments, it is especially important to address cases with uncertain processing times. This paper addresses the four-machine flowshop scheduling problem to minimize TCT with uncertain processing times. Due to the NP-hardness of the problem, different algorithms were presented as solutions in scheduling literature. In this paper, a new substantially improved algorithm is proposed and parameters of the algorithm are fine tuned. The proposed algorithm is compared to the best existing algorithm (RAIRO Operations Research 54, 529–553, 2020) in scheduling literature using extensive computational experiments and statistical analysis. Computational methods using the programming language python, along with statistical inference, is used to confirm the effectiveness of the proposed algorithm over the existing ones. Computational methods reveal that the proposed algorithm is, on average, 86.8% more effective than the best existing one in literature with similar computational times. A test of hypothesis further confirms the effectiveness of the proposed algorithm with a p-value of less than 0.00001, which is practically zero.
  • [ X ]
    Öğe
    An Improved Algorithm for Minimizing Makespan on Flowshops with Uncertain Processing Times
    (Kırıkkale Üniversitesi, 2021) Allahverdi, Ali; Allahverdi, Muberra
    We address the four-machine flowshop scheduling problem with the objective of minimizing makespan with uncertain processing times The problem was addressed in the literature (RAIRO Operations Research 54, 529-553) and different algorithms were proposed. In this paper, we propose a new algorithm for the problem. The new proposed algorithm is compared with the best algorithm in the literature by using extensive computational experiments. Computational experiments indicate that the new proposed algorithm performs much better than the best algorithm in the literature in terms of error while both have the same computational time. Specifically, the new proposed algorithm reduces the error of the best existing algorithm in the literature over 40%. This result has been confirmed by using tests of hypotheses with a significance level of 0.01.