Production and Operations Management · 1993 · 401 citations · 71 references
BusinessComputer ScienceSystems EngineeringProduction PlanningQueuing TheoryStochastic ProcessesMulti-server QueuesQueueing AnalysisQueueing ProcessesQueueing SystemsQueueing TheoryWaiting Time AnalysisAnalytic ApproximationsCongestion ModellingCustomer Waiting TimeIdentically Distributed Random VariablesInterarrival Time DistributionsInterarrival TimesQueueing ModelsWaiting Time Distributions
Queueing models represent production systems with congestion from irregular flows, but exact analyses are difficult. The paper develops and evaluates simple approximations for the GI/G/m queue. The GI/G/m queue has m parallel identical servers, FCFS discipline, and general interarrival and service time distributions; the approximations use only the first two moments of these distributions to estimate expected waiting time, waiting probability, and other congestion measures. The approximations provide useful, rapid estimates that complement exact algorithms and can serve as building blocks for more complex systems and larger network models.
Queueing models can usefully represent production systems experiencing congestion due to irregular flows, but exact analyses of these queueing models can be difficult. Thus it is natural to seek relatively simple approximations that are suitably accurate for engineering purposes. Here approximations for a basic queueing model are developed and evaluated. The model is the GI/G/m queue, which has m identical servers in parallel, unlimited waiting room, and the first‐come first‐served queue discipline, with service and interarrival times coming from independent sequences of independent and identically distributed random variables with general distributions. The approximations depend on the general interarrival‐time and service‐time distributions only through their first two moments. The main focus is on the expected waiting time and the probability of having to wait before beginning service, but approximations are also developed for other congestion measures, including the entire distributions of waiting time, queue‐length and number in system. These relatively simple approximations are useful supplements to algorithms for computing the exact values that have been developed in recent years. The simple approximations can serve as starting points for developing approximations for more complicated systems for which exact solutions are not yet available. These approximations are especially useful for incorporating GI/G/m models in larger models, such as queueing networks, wherein the approximations can be components of rapid modeling tools.
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