For nearly all call centers, agent schedules are typically created several days or weeks before the time that agents report to work. After schedules are created, call center resource managers receive additional information that can affect forecasted workload and resource availability. In particular, there is significant evidence, both among practitioners and in the research literature, suggesting that actual call arrival volumes early in a scheduling period (typically an individual day or week) can provide valuable information about the call arrival pattern later in the same scheduling period. In this paper, we develop a flexible and powerful heuristic framework for managers to make intra‐day resource adjustment decisions that take into account updated call forecasts, updated agent requirements, existing agent schedules, agents' schedule flexibility, and associated incremental labor costs. We demonstrate the value of this methodology in managing the trade‐off between labor costs and service levels to best meet variable rates of demand for service, using data from an actual call center.

A large, customer-focused software company relied on simulation modeling of its call center operations in launching a new fee-based technical-support program. Prior to launching this rapid program, call center managers were concerned about the difficulty of meeting a proposed guarantee to paying customers that they would wait less than one minute on hold. Managers also wanted to know how the new program would affect the service provided to their existing base of regular, nonpaying customers. We quickly developed an animated simulation model that addressed these concerns and gave the managers a good understanding for the impact on system performance of changes in the number of customers purchasing the rapid program and in the number of agents. The one-minute guarantee would be fairly easy to achieve, even if the percentage of callers in the rapid program became quite high. Managers also gained confidence that, with appropriate staffing levels, they could successfully implement the new program, which they soon did.

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This paper describes an animated simulation model which was used to assess several strategies for improving urban on- street parking systems. Built in ARENA, the model tracks six key performance measures including the probability of finding a parking space, the time spent searching for a parking space, and the total amount of money put into parking meters. Sensitivity analyses performed with the model revealed that a driver's chances of finding a parking space increase at about the same rate as decreases in the mean actual parking time. The model was also used to assess the effects of various degrees of enforcement of the one-hour time limit on system performance. Finally, experiments were performed with the model to determine how newer meter technologies might affect parking revenues collected by city management. Specifically, when existing meters were replaced by resetting meters which zero out any time left after a car pulls out of a space, parking meter revenues rose by 23 %.

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Administrators should consider the use of a DHA and/or private rooms to ease the census strains of pediatric units and the resultant revenue of this service.

In this paper, we explore the challenges and opportunities we face teaching computer simulation at a business school. While our students tend not to be as technically savvy as most engineering students, at times limiting the technical complexity of what we can cover in our courses, we are able to use simulation as a tool to analyze and discuss business problems in-depth. We explore the differences both between business and non-business simulation courses, as well as those between our graduate and undergraduate courses.

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