Yazar "Maihami, Reza" seçeneğine göre listele

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    A robust Tri-Objective optimization to supply chain configuration under Vendor-Managed inventory policy considering supply chain visibility
    (Pergamon-Elsevier Science Ltd, 2023) Golpira, Heris; Tirkolaee, Erfan Babaee; Maihami, Reza; Karimi, Kajal
    This paper presents a novel tri-objective robust Mixed-Integer Linear Programming (MILP) to a two-echelon supply chain configuration. It simultaneously considers the Vendor-Managed Inventory (VMI) policy and the Supply Chain Visibility (SCV), under uncertain demand. The objectives are to concurrently maximize the total visibility, minimize the number of deficient products and minimize the total cost. The LP-metric is then applied to deal with the tri-objectiveness of the model. The results show that the proposed approach successfully es-tablishes robustness by deteriorating the optimal values of the objective functions as the cost of robustness. A sensitivity analysis reveals that the level of uncertainty has the greatest impact on the network's cost and deficiency of objective functions. However, the greatest effect on the SCV function is the change in the lower visibility threshold parameter. Moreover, the value of the deficiency rate parameter has the least impact on all three objective functions and has no impact on the visibility of the supply chain.
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    A socio-economic optimization model for blood supply chain network design during the COVID-19 pandemic: an interactive possibilistic programming approach for a real case study
    (Elsevier Ltd., 2022) Tirkolaee, Erfan Babaee; Golpîra, Hêris; Javanmardan, Ahvan; Maihami, Reza
    In uncertain circumstances like the COVID-19 pandemic, designing an efficient Blood Supply Chain Network (BSCN) is crucial. This study tries to optimally configure a multi-echelon BSCN under uncertainty of demand, capacity, and blood disposal rates. The supply chain comprises blood donors, collection facilities, blood banks, regional hospitals, and consumption points. A novel bi-objective Mixed-Integer Linear Programming (MILP) model is suggested to formulate the problem which aims to minimize network costs and maximize job opportunities while considering the adverse effects of the pandemic. Interactive possibilistic programming is then utilized to optimally treat the problem with respect to the special conditions of the pandemic. In contrast to previous studies, we incorporated socio-economic factors and COVID-19 impact into the BSCN design. To validate the developed methodology, a real case study of a Blood Supply Chain (BSC) is analyzed, along with sensitivity analyses of the main parameters. According to the obtained results, the suggested approach can simultaneously handle the bi-objectiveness and uncertainty of the model while finding the optimal number of facilities to satisfy the uncertain demand, blood flow between supply chain echelons, network cost, and the number of jobs created.