Now showing items 1-7 of 7
A universal appointment rule in the presence of no-shows and walk-ins
This study introduces a universal “Dome” appointment rule that can be parameterized through a planning constant for different clinics characterized by the environmental factors—no-shows, walk-ins, number of appointments ...
Communicating forecasts: the simplicity of simulated experience
It is unclear whether decision makers who receive forecasts expressed as probability distributions over outcomes understand the implications of this form of communication. We suggest a solution based on the fact that people ...
A universal appointment rule with patient classification for service times, no-shows and walk-ins
This study evaluates patient classification for scheduling and sequencing appointments for patients differentiated by their mean and standard deviation of service times, no-show, and walk-in probabilities. Alternative ...
The golden rule of forecasting: objections, refinements, and enhancements
In providing a “golden rule” for forecasting, Armstrong, Green, and Graefe (this issue) raise aspirations that reliable forecasting is possible. They advocate a conservative approach that mainly involves extrapolating from ...
Predicting the performance of queues–A data analytic approach
Existing models of multi-server queues with system transience and non-standard assumptions are either too complex or restricted in their assumptions to be used broadly in practice. This paper proposes using data analytics, ...
Assessment of patient classification in appointment system design
(Production and Operations Management Society, 2008-05)
This paper investigates two approaches to patient classification: using patient classification only for sequencing patient appointments at the time of booking and using patient classification for both sequencing and ...
Providing information for decision making: Contrasting description and simulation
Providing information for decision making should be like telling a story. You need to know, first, what you want to say; second, whom you are addressing; and third, how to match the message and audience. However, data ...
Share this page