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Can I read Workload Prediction for Improved Design and Reliability of Complex Systems on EtoBox?
Workload Prediction for Improved Design and Reliability of Complex Systems by Andreas Gregoriades; Alistair Sutcliffe is a Engineering article available to read on EtoBox.
What is Workload Prediction for Improved Design and Reliability of Complex Systems about?
This paper describes a method and a tool for analysing and predicting workload for the design and reliability of complex sociotechnical systems. It concentrates on the need to assess workload early in the design phase to prevent systems failures. This is a continuation of our previous work on workload assessment. The method is supported by a tool that enables scenario-based validation of prospective socio-technical systems designs such as command and control rooms of military vessels. The approach combines probabilistic measures of human performance with subjective estimates of workload. The causal relationships of performance shaping factors (PSF) are modelled in a Bayesian belief network (BBN) and used to assess the agent's operational performance and reliability. Workload for each agent is calculated based on demand placed upon agents in terms of behavioural response to tasks, communications and interactions between humans and technology. The approach uses scenarios to stress test prospective system designs, where each scenario is modelled as a sequence of events. Reliability is expressed in terms of human error and is dynamically assessed throughout test scenario executions usi
Who reads Workload Prediction for Improved Design and Reliability of Complex Systems?
It is typically read by researchers, students, and practitioners in Engineering.
- Author
- Andreas Gregoriades; Alistair Sutcliffe
- Publisher
- Elsevier Science; Elsevier ; Elsevier BV (ISSN 0951-8320)
- Published
- 2008
- Language
- EN
- Field
- Engineering (Physical Sciences)