Complex service systems that involve humans, such as call centers, health-care systems, emergency systems, etc., contain important uncertainty that is difficult to model in a realistic way. For example, arrivals in call centers follow stochastic processes whose rates are themselves random and depend significantly on the time of the day, type of day (day of the week, holiday), period of the year, weather, other external events, etc. The arrival processes of different call types may also be dependent. Call durations (service times) have distributions that depend on the call type and on the particular agent who handles the call, and are often time-dependent because the effectiveness of agents depends on their experience, base qualities, motivation, fatigue, etc. The way customers abandon before getting a response, perhaps call again later in the day, etc., is also not obvious to model. Much of the difficulty comes from the fact that these random variables are dependent. In the talk, we will illustrate these types of modeling problems with concrete examples and data, and will review some models, ideas, and results.
Once we have a model that can be simulated to estimate performances measures of interest, the next step is to use it for optimization, e.g., optimize the number, types, and work schedules of agents that answer the calls, the priority rules or routing rules for calls of different types, etc., given some objective function and constraints that depend on the performance measures and budget. We will briefly discuss these optimization problems.
Coffee and biscuits will be offered at the beginning of the seminar.
Welcome to everyone!