Learning in mitral valve repair surgery using telemanipulative technology occurs at the East Carolina Heart Institute according to a logarithmic curve, with a learning percentage of 95%. From our regression output, we can make an approximate prediction of total robot time using an additive model. These metrics can be used by programs for benchmarking to manage the implementation of this new technology, as well as for capacity planning, scheduling, and capital budget analysis.
In this article, we examine the problem of scheduling a service operation; that is, defining the set of shifts for workers that best meets the demand profile, and of assigning individual workers to those shifts. Tour scheduling identifies the number of agents assigned to specific start and end times over the course of the planning period. Rostering is the process of assigning individual agents to specific shift schedules. In this article, we develop a basic example based on a call center application. We discuss how the problem can be formulated and solved as an integer program. We then discuss how the problem can grow unwieldy as staffing flexibility increases and discuss approaches for dealing with that problem. Finally, we identify issues that arise in other, noncall center applications.
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