A semi-markov approach to stochastic compartmental models

In the past, various methods using either differential equations or differential-difference equations have been used to analyze stochastic compartmental models. In this paper a semi-Markov process approach is used to provide a framework for analyzing such models. The distribution function of the number of particles in each of the compartments is derived along with the stationary distributions. Various models found in the literature arising from biological and reliability applications are analyzed here using the semi-Markov process technique.     

Coping with Time‐Varying Demand When Setting Staffing Requirements for a Service System

We review queueing‐theory methods for setting staffing requirements in service systems where customer demand varies in a predictable pattern over the day. Analyzing these systems is not straightforward, because standard queueing theory focuses on the long‐run steady‐state behavior of stationary models. We show how to adapt stationary queueing models for use in nonstationary environments so that time‐dependent performance is captured and staffing requirements can be set. Relatively little modification of straightforward stationary analysis applies in systems where service times are short and the targeted quality of service is high. When service times are moderate and the targeted quality of service is still high, time‐lag refinements can improve traditional stationary independent period‐by‐period and peak‐hour approximations. Time‐varying infinite‐server models help develop refinements, because closed‐form expressions exist for their time‐dependent behavior. More difficult cases with very long service times and other complicated features, such as end‐of‐day effects, can often be treated by a modified‐offered‐load approximation, which is based on an associated infinite‐server model. Numerical algorithms and deterministic fluid models are useful when the system is overloaded for an extensive period of time. Our discussion focuses on telephone call centers, but applications to police patrol, banking, and hospital emergency rooms are also mentioned.     

The Maximum Throughput on a Golf Course

We develop stochastic models to help manage the pace of play on a conventional 18‐hole golf course. These models are for group play on each of the standard hole types: par‐3, par‐4, and par‐5. These models include the realistic feature that k−2 groups can be playing at the same time on a par‐k hole, but with precedence constraints. We also consider par‐3 holes with a “wave‐up” rule, which allows two groups to be playing simultaneously. We mathematically determine the maximum possible throughput on each hole under natural conditions. To do so, we analyze the associated fully loaded holes, in which new groups are always available to start when the opportunity arises. We characterize the stationary interval between the times successive groups clear the green on a fully loaded hole, showing how it depends on the stage playing times. The structure of that stationary interval evidently can be exploited to help manage the pace of play. The mean of that stationary interval is the reciprocal of the capacity. The bottleneck holes are the holes with the least capacity. The bottleneck capacity is then the capacity of the golf course as a whole.