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    Navigating the new normal: Redefining N95 respirator design with an integrated text mining and quality function deployment-based optimization model
    (Pergamon-Elsevier Science Ltd, 2024) Gangadhari, Rajan Kumar; Tarei, Pradeep Kumar; Chand, Pushpendu; Rabiee, Meysam; Delen, Dursun
    The global surge in demand during the pandemic led to a proliferation of substandard surgical N95 respirators, particularly in the e-commerce market, due to a lack of regulatory standards. In the post-pandemic era, the essential role of these respirators in protecting against airborne hazards and infectious agents persists. Addressing the need for standardized design and manufacturing processes, this research proposes a three-phased integrated decision-making framework. Firstly, text-mining algorithms analyze online consumer reviews to identify critical characteristics of surgical N95 respirators. Subsequently, a spherical-fuzzy-based quality function deployment assesses manufacturing capabilities in alignment with consumer requirements. The final phase utilizes Mixed Integer Non-linear Programming to optimize utility and select the most effective design, considering both technical specifications and consumer needs. The proposed framework is validated through a case study, revealing technical characteristics crucial for organizations in product development. The findings not only offer insights into the technical aspects of N95 respirators but also recommend essential product characteristic tests during manufacturing and certification. This information is vital for educating consumers and guiding companies in the effective use and development of Surgical N95 respirators in the post-pandemic landscape, addressing the challenges posed by inferior and counterfeit products.
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    An optimization framework for the sustainable healthcare facility location problem using a hierarchical conflict resolution approach
    (Springer, 2023) Aslani, Babak; Rabiee, Meysam; Jabbari, Mona; Delen, Dursun
    Optimal determination of healthcare facility locations is among critical strategic decisions that significantly impact long-term public health, well-being, and social welfare. In addition to the inherent complexities that arise from resource allocation and budget limitations in general facility location problems, this problem in the healthcare sector is even more complex. On one hand, since decision makers should consider various criteria in all aspects of sustainability in locating these facilities, this problem is a complex Multi-criteria decision-making problem. On the other hand, other objectives should also be considered in locating the mentioned facilities. As a result, this paper aims to develop a multi-objective mixed-integer linear programming (MOMILP) model, one aspect of which is the Multi-criteria decision-making (MCDM) aspect of the problem by considering sustainable criteria for the healthcare facility location problem. We defined total travel distance, equity, local covering, effectiveness, and overlap functions as the objective functions of the developed model. A novel hierarchical conflict-resolution approach is included to rank the sub-criteria as a guideline for the Best-Worst Method (BWM) to find the weights of the criteria. To examine the effectiveness of the proposed model, we applied it to a real-world problem of locating preventive healthcare centers in Iran. As the final stage of the study, a sensitivity analysis was carried out to test the performance of the proposed framework in different possible scenarios. The results indicated that the approach is robust and applicable to real-life facility-location problems.