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As an abrupt epidemic occurs, healthcare systems are shocked by the surge in the number of susceptible patients' demands, and decision-makers mostly rely on their frame of reference for urgent decision-making. Many reports have declared the COVID-19 impediments to trading and global economic growth. This study aims to provide a mathematical model to support pharmaceutical supply chain planning during the COVID-19 epidemic. Additionally, it aims to offer new insights into hospital supply chain problems by unifying cold and non-cold chains and considering a wide range of pharmaceuticals and vaccines. This approach is unprecedented and includes an analysis of various pharmaceutical features such as temperature, shelf life, priority, and clustering. To propose a model for planning the pharmaceutical supply chains, a mixed-integer linear programming (MILP) model is used for a four-echelon supply chain design. This model aims to minimize the costs involved in the pharmaceutical supply chain by maintaining an acceptable service level. Also, this paper considers uncertainty as an intrinsic part of the problem and addresses it through the wait-and-see method. Furthermore, an unexplored unsupervised learning method in the realm of supply chain planning has been used to cluster the pharmaceuticals and the vaccines and its merits and drawbacks are proposed. A case of Tehran hospitals with real data has been used to show the model's capabilities, as well. Based on the obtained results, the proposed approach is able to reach the optimum service level in the COVID conditions while maintaining a reduced cost. The experiment illustrates that the hospitals' adjacency and emergency orders alleviated the service level significantly. The proposed MILP model has proven to be efficient in providing a practical intuition for decision-makers. The clustering technique reduced the size of the problem and the time required to solve the model considerably. • Merging cold and non-cold chains to help hospitals handling supply chain challenges. • Supporting decision-makers during epidemics by proposing a novel mathematical model. • Considering various pharmaceuticals and their features using K-means clustering. • Multi-commodity supply chain benefits from unsupervised learning for clustering. • Considering features of various phases of epidemics by adjusting model parameters. [ABSTRACT FROM AUTHOR]

The COVID-19 (Corona virus disease 2019) pandemic continues to slash through the entire humanity on the earth causing an international health crisis and financial uncertainty. The pandemic has formed a colossal disruption in supply chain networks. It has caused piling higher mortality in patients with comorbidities and generated a surging demand for critical care equipment, vaccines, pharmaceuticals, and cutting-edge technologies. Personal protective equipment, masks, ventilators, testing kits, and even commodities required for daily care have been scarce as lockdown and social distancing guidelines have kicked in. Amidst COVID-19, implementing and executing key processes of the healthcare supply chain (HSC) in a secured, trusted, effective, universally manageable, and the traceable way is perplexing owing to the fragile nature of the HSC, which is susceptible to redundant efforts and systemic risks that can lead to adverse impacts on consumer health and safety. Though the crisis shone a harsh light on the cracks and weaknesses of the HSC, it brings some significant insights into how HSC can be made more resilient and how healthcare industries figure out solutions to mitigate disruptions. While there are innumerable experiences learned from the disruption of this crisis, in this paper, five important areas to analyze the most vital and immediate HSC enhancements including building a resilient supply chain, thinking localization, implementing reliable reverse logistics, breaking down extant silos to achieve end-to-end visibility, and redesigning HSC using digitalization are emphasized. This work identifies important features related to CoT and HSC. Also, this study links these lessons to a potential solution through Chain of Things (CoT) technology. CoT technology provides a better way to monitor HSC products by integrating the Internet of Things (IoT) with blockchain networks. However, such an integrated solution should not only focus on the required features and aspects but also on the correlation among different features. The major objective of this study is to reveal the influence path of CoT on smart HSC development. Hence, this study exploits (i) fuzzy set theory to eliminate redundant and unrelated features; (ii) the Decision-Making and Experimental Evaluation Laboratory (DEMATEL) method to handle the intricate correlation among different features. This fuzzy-DEMATEL (F-DEMATEL) model attempts to direct CoT technology towards smart HSC by identifying the most influencing factors and investors are recommended to contribute to the development of application systems. This work also demonstrates how CoT can act a vital role in handling the HSC issues triggered by the pandemic now and in the post-COVID-19 world. Also, this work proposes different CoT design patterns for increasing opportunities in the HSC network and applied them as imperative solutions for major challenges related to traditional HSC networks. • The Coronavirus disease has formed a colossal disruption in healthcare supply chain networks. • Resilience, localization, reverse logistics, and digitalization are considered to return to a "new normal". • Leveraging the nexus between Internet-of-things and blockchain technology. • Direct COT technology towards smart HSC networks by identifying most influencing factors. • Fuzzy-DEMATEL approach to identify the influence path of COT on HSC networks. [ABSTRACT FROM AUTHOR]