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    A multi-objective optimization approach for resource allocation and transportation planning in institutional quarantine centres
    (Springer, 2024) Biswas, Saptadeep; Belamkar, Prasad; Sarma, Deepshikha; Tirkolaee, Erfan Babaee; Bera, Uttam Kumar
    The COVID-19 pandemic has profoundly impacted global logistics and supply chains, resulting in widespread disruptions due to outbreaks, labour shortages, and geopolitical risks. These challenges have cascaded into substantial disturbances within supply chains, sparking global inflation. In response to these urgent issues, this study presents a robust mathematical model to optimize resource allocation and logistics within COVID-19 quarantine centres (QCs). The primary objectives of this model are twofold: first, to minimize costs, and second, to ensure the efficient delivery of patient care. It considers various critical constraints, including facility capacity, patient arrivals, and resource utilization. This holistic framework is designed to tackle the intricate challenges of managing QCs during the pandemic. This study extends its focus beyond the fundamentals of quarantine centre management by prioritizing the reduction of carbon emissions in transportation. A dedicated transportation plan has been developed to simultaneously lower costs and save time, contributing to the overall efficiency of the logistics process. The multifaceted objective functions considered in the model encompass procurement, labour, transportation costs, setup, management, and food provisioning. To strike a balanced compromise among these diverse objectives, the study leverages a range of optimization techniques. These include the Weighted Average Method (WAM), Epsilon Constraint Method (ECM), Goal Programming Approach (GPA), Interactive Fuzzy Satisfying Technique (IFST), Weighted Tchebychef Metrics Programming (WTMP), Global Criterion Method (GCM), Neutrosophic Fuzzy Linear Programming Approach (NFLPA), Spherical Fuzzy Linear Programming Approach (SFLPA), and Non-dominated Sorting Genetic Algorithm (NSGA-II). By adopting this comprehensive approach, the study aims to bolster the resilience of global supply chains, ensure the effective allocation of resources during the COVID-19 pandemic, and significantly reduce carbon emissions, thus contributing to a more sustainable and secure future.
  • Küçük Resim
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    A multi-objective optimization approach for resource allocation and transportation planning in institutional quarantine centres
    (Springer, 2024) Biswas, Saptadeep; Belamkar, Prasad; Sarma, Deepshikha; Tirkolaee, Erfan Babaee; Bera, Uttam Kumar
    The COVID-19 pandemic has profoundly impacted global logistics and supply chains, resulting in widespread disruptions due to outbreaks, labour shortages, and geopolitical risks. These challenges have cascaded into substantial disturbances within supply chains, sparking global inflation. In response to these urgent issues, this study presents a robust mathematical model to optimize resource allocation and logistics within COVID-19 quarantine centres (QCs). The primary objectives of this model are twofold: first, to minimize costs, and second, to ensure the efficient delivery of patient care. It considers various critical constraints, including facility capacity, patient arrivals, and resource utilization. This holistic framework is designed to tackle the intricate challenges of managing QCs during the pandemic. This study extends its focus beyond the fundamentals of quarantine centre management by prioritizing the reduction of carbon emissions in transportation. A dedicated transportation plan has been developed to simultaneously lower costs and save time, contributing to the overall efficiency of the logistics process. The multifaceted objective functions considered in the model encompass procurement, labour, transportation costs, setup, management, and food provisioning. To strike a balanced compromise among these diverse objectives, the study leverages a range of optimization techniques. These include the Weighted Average Method (WAM), Epsilon Constraint Method (ECM), Goal Programming Approach (GPA), Interactive Fuzzy Satisfying Technique (IFST), Weighted Tchebychef Metrics Programming (WTMP), Global Criterion Method (GCM), Neutrosophic Fuzzy Linear Programming Approach (NFLPA), Spherical Fuzzy Linear Programming Approach (SFLPA), and Non-dominated Sorting Genetic Algorithm (NSGA-II). By adopting this comprehensive approach, the study aims to bolster the resilience of global supply chains, ensure the effective allocation of resources during the COVID-19 pandemic, and significantly reduce carbon emissions, thus contributing to a more sustainable and secure future. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.