Scopus İndeksli Yayınlar Koleksiyonu

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Browsing Scopus İndeksli Yayınlar Koleksiyonu by Author "A. P. S. Rathore"

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    Citation - WoS: 4
    Citation - Scopus: 7
    A machine learning-based hybrid approach for maximizing supply chain reliability in a pharmaceutical supply chain
    (PERGAMON-ELSEVIER SCIENCE LTD, 2025) Devesh Kumar; Gunjan Soni; Sachin Kumar Mangla; Yigit Kazancoglu; A. P. S. Rathore; Rathore, A. P. S.; Kumar, Devesh; Soni, Gunjan; Mangla, Sachin Kumar; Kazancoglu, Yigit
    In today's interconnected global economy supply chain (SC) reliability is crucial particularly in sectors like the pharmaceutical industry where disruptions can significantly impact public health. SCs have become important to industries due to a customer-driven shift aimed at improving SC reliability especially in terms of delivery performance. It is crucial to define and find the best strategy for reaching the organizational objectives in SC. While designing a SC supplier selection (SS) and order allocation are two decisions that have to be made separately. This study addresses the critical challenges of SS and order allocation within pharmaceutical SCs. It proposes a novel two-phased hybrid approach the first phase integrates machine learning(ML) and multicriteria decision-making(MCDM) method for robust SS. The second phase develops a mathematical model to optimize order allocation while considering SC reliability. This work employs support vector machine (SVM) as the particular ML method in which the training data are historical corporate data that dictate parameters weights. These weights are then used in the measurement of alternatives and ranking according to compromise solution (MARCOS) method to rank the suppliers. A multi- objective mixed integer programming (MOMIP) model is then formulated to identify the right order quantity from the identified suppliers of a pharmaceutical SC in order to minimize SC cost and maximize SC reliability. The results indicate that by optimizing SC reliability and costs orders are directed to high-priority suppliers. This study provides a comprehensive data-driven decision-making framework to assure SC's reliability and cost-efficiency. The implications of the findings are also profound and contribute valuable insights for industry practitioners to improve the performance of SC. To illustrate the proposed methodology an SC example of a pharmaceutical industry is analyzed using the LINGO solver.
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    Citation - WoS: 19
    Citation - Scopus: 18
    Integrating resilience and reliability in semiconductor supply chains during disruptions
    (ELSEVIER, 2024) Devesh Kumar; Gunjan Soni; Sachin Kumar Mangla; Jiajia Liao; A. P. S. Rathore; Yigit Kazancoglu; Rathore, A. P. S.; Soni, Gunjan; Kumar, Devesh; Liao, Jiajia; Kazancoglu, Yigit; Mangla, Sachin Kumar
    The semiconductor industry a cornerstone of modern technology has been crucial in driving globalization and supporting various sectors from consumer electronics to automotive industries. However in recent years the industry has faced substantial challenges threatening its ability to meet the surging demand for semiconductor chips. Disruptions at any point in the supply chain from raw material sourcing to end-product delivery can substantially influence the semiconductor ecosystem. The intricate nature of such SCs makes them highly vulnerable to various disruptions emphasizing the critical need for building resilient and reliable supply chain strategies. This article presents comprehensive research aimed at addressing critical gaps in the understanding and management of resilience and reliability within the semiconductor supply chain (SSC). This study proposes a multi-objective mixed-integer non-linear programming (MO-MINLP) model to configure an SSC while considering reliability and resilience measures. It emphasizes and draws attention to the importance of resilience and reliability in managing SSC disruptions during a pandemic and potential future epidemic outbreak. Exploring the precise breakdown of batch transportation between two sites shows how disruption can affect product flow along the SC. The applicability of the proposed method is demonstrated through a numerical example of an SSC solved using the LINGO solver. Finally a sensitivity analysis is conducted on the model's parameters to assess the capability and effectiveness of the results from managerial viewpoints.