Assessing Pilot Workload in Single-Pilot Operations with Advanced Autonomy

The proposed transition to single-pilot operations (SPO) in commercial and military aircraft has motivated the development of advanced autonomy systems. However, a detailed analysis of the impact of advanced autonomy on pilot workload through various phases of flight and contingency scenarios has not been conducted. To this end, this paper presents the development of the Pilot-Autonomy Workload Simulation (PAWS), a discrete event simulation model that allows the investigation of pilot workload under a variety of advanced autonomy capabilities and scenarios. Initial utilization results from PAWS of nominal and off-nominal point-to-point missions demonstrate that the workload for a single pilot assisted by advanced autonomy varies considerably over different phases of flight and various contingencies. These results suggest that advanced autonomy to offset pilot workload is not needed for low-workload phases, but could be critical during periods of high workload.

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The Impact of One-Dose Package of Medicines on Patient Waiting Time in Dispensing Pharmacy: Application of a Discrete Event Simulation Model

Biological and Pharmaceutical Bulletin ◽

10.1248/bpb.b17-00781 ◽

2018 ◽

Vol 41 (3) ◽

pp. 409-418

Author(s):

Daisuke Furushima ◽

Hiroshi Yamada ◽

Michiko Kido ◽

Yuko Ohno

Keyword(s):

Simulation Model ◽

Discrete Event Simulation ◽

Waiting Time ◽

Discrete Event ◽

Event Simulation ◽

Discrete Event Simulation Model ◽

Patient Waiting Time ◽

The Impact ◽

Patient Waiting

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

PLoS ONE ◽

10.1371/journal.pone.0253869 ◽

2021 ◽

Vol 16 (6) ◽

pp. e0253869

Author(s):

Michael Saidani ◽

Harrison Kim ◽

Jinju Kim

Keyword(s):

Simulation Model ◽

Discrete Event Simulation ◽

Discrete Event ◽

Daily Basis ◽

Health Crisis ◽

Event Simulation ◽

Discrete Event Simulation Model ◽

Rapid Result ◽

The Usa ◽

The Impact

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.

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