ABSTRACT
The outbreak of the COVID pandemic revealed that supply chains are not resilient to such a type of turmoil, and the food industry appeared to be particularly vulnerable. Meanwhile, customers expect uninterrupted deliveries and the products' selection responding to their preferences. In this article, we discuss several topics related to supply management that allow preparing a delivery plan for a distributed network of vending machines, considering each location individually. The developed solution takes advantage of the state-of-the-art machine learning methods. However, it is human-centric and aligned with the concept of Industry 5.0. We present the conceptual and technological side of the solution with a particular emphasis on the developed feature extraction framework, which uses selected indicators from the survival analysis. We present an analysis of the real data confirming that the proposed approach copes well with high uncertainty in data, addressing the cold-start problem. © 2005-2012 IEEE.
ABSTRACT
Currently, the scenario of a self-contained disappearance of the epidemic (as it was in the case of SARS) is no longer taken into consideration, whilst the SARS-CoV-2 virus will stay with us forever, similarly to other coronaviruses or flu. It is quite likely that periodical exacerbations of the epidemics – their growth and decrease – depend on many factors, which comprise, among others, the approval of the restrictions by the society or the manner in the epidemiological supervision is carried out and whether it is consistent. We must be ready for about 18–24 months of a high activity of COVID-19 with periodic active hot spots in many world regions. This requires efficient health services and the access to efficacious medication. Without an effective prophylactic vaccine, it seems that we will not be able to prevent the spread of the pandemic © Polskie Towarzystwo Onkologiczne