ABSTRACT
With the world facing a public health emergency due to the Coronavirus disease (COVID-19) in a global pandemic, this paper provides insight about how a simulation model was used to determine the impact of headcount variability during lockdown on fab performance. To create a robust simulation model, operator loading time was introduced as one of the input parameters. An existing and well validated Discrete Event Fab simulation model was extended with operator modelling, and was used to conduct case studies, evaluating the impact of different operator availability scenarios including work disruptions for several shifts within a week. The studies provide implications for operation to derive mitigation strategies, weighing the trade-off between cost demand and speed loss due to operator resources. © 2021 IEEE.
ABSTRACT
Strict social distancing rules are being implemented to stop the spread of COVID-19 pandemic in many cities globally, causing a sudden and extreme change in the transport activities. This offers a unique opportunity to assess the effect of anthropogenic activities on air quality and provides a valuable reference to the policymakers in developing air quality control measures and projecting their effectiveness. In this study, we evaluated the effect of the COVID-19 lockdown on the roadside and ambient air quality in Hong Kong, China, by comparing the air quality monitoring data collected in January-April 2020 with those in 2017-2019. The results showed that the roadside and ambient NO2, PM10, PM2.5, CO and SO2 were generally reduced in 2020 when comparing with the historical data in 2017-2019, while O3 was increased. However, the reductions during COVID-19 period (i.e., February-April) were not always higher than that during pre-COVID-19 period (i.e., January). In addition, there were large seasonal variations in the monthly mean pollutant concentrations in every year. This study implies that one air pollution control measure may not generate obvious immediate improvements in the air quality monitoring data and its effectiveness should be evaluated carefully to eliminate the effect of seasonal variations. © 2020 by the authors.