Circular economy as a key for industrial value chain resilience in a post-COVID world: what do future engineers think?

While the COVID-19 pandemic has led to many disruptions in industrial value chains, the adoption of circular economy (CE) principles appears to be a commendable solution for more robust, resilient, and sustainable industrial supply chains. In this study, the standpoints and visions of two consecutive classes of engineering students - following the course "Circular Economy & Industrial Systems" at the Université Paris-Saclay - are given on how they value CE strategies to mitigate the impact of COVID-19 on industrial practices. Capturing and understanding the viewpoints of the engineers of tomorrow on such a pressing issue is key to train and provide them with the suitable methods and tools to build a more circular and sustainable society. At the end of their eight-week training class, including theoretical background on industrial ecology tools, workshops, and a hands-on project, part of the final exam included a one-hour essay in which the students had to argue their position on the following questions: (i) "Circular Economy as an answer to the COVID-19 crisis?" for the class of 2020, and (ii) "Circular Economy as an answer for green recovery and value chain resiliency in the COVID-19 context?" for the class of 2021. Interestingly, the evolution of viewpoints between the beginning of the COVID-19 crisis (exam conducted in May 2020 for the first class) and one year after (exam conducted in Mars 2021 for the second class) is discussed and illustrated. Also, the answers and insights provided by engineering students on these questions are positioned within the state-of-the-art literature on the topic. Last but not least, key recommendations and challenges on how CE could alleviate COVID-related disruptions and production shortages are synthesized in a SWOT (strengths, weaknesses, threats, and opportunities) diagram.

Designing optimal COVID-19 testing stations locally: A discrete event simulation model applied on a university campus.

Providing sufficient testing capacities and accurate results in a time-efficient way are essential to prevent the spread and lower the curve of a health crisis, such as the COVID-19 pandemic. In line with recent research investigating how simulation-based models and tools could contribute to mitigating the impact of COVID-19, a discrete event simulation model is developed to design optimal saliva-based COVID-19 testing stations performing sensitive, non-invasive, and rapid-result RT-qPCR tests processing. This model aims to determine the adequate number of machines and operators required, as well as their allocation at different workstations, according to the resources available and the rate of samples to be tested per day. The model has been built and experienced using actual data and processes implemented on-campus at the University of Illinois at Urbana-Champaign, where an average of around 10,000 samples needed to be processed on a daily basis, representing at the end of August 2020 more than 2% of all the COVID-19 tests performed per day in the USA. It helped identify specific bottlenecks and associated areas of improvement in the process to save human resources and time. Practically, the overall approach, including the proposed modular discrete event simulation model, can easily be reused or modified to fit other contexts where local COVID-19 testing stations have to be implemented or optimized. It could notably support on-site managers and decision-makers in dimensioning testing stations by allocating the appropriate type and quantity of resources.

A Discrete Event Simulation-Based Model to Optimally Design and Dimension Mobile COVID-19 Saliva-Based Testing Stations.

SUMMARY STATEMENT: The present COVID-19 brief report addresses: (1) the problem of optimal design and resource allocation to mobile testing stations to ensure rapid results to the persons getting tested; (2) the proposed solution through a newly developed discrete event simulation model, experienced in on-campus saliva-based testing stations at the University of Illinois at Urbana-Champaign; and (3) the lessons learned on how 10,000 samples (from noninvasive polymerase chain reaction COVID-19 tests) can be processed per day on campus, as well as how the model could be reused or adapted to other contexts by site managers and decision makers.