No abstract

There has been frequent controversy over the years regarding the use of numerical rootfinding for the solution of queueing problems. It has been said that such problems quite often present computational difficulties. However, it turns out that rootfinding in queueing is so well structured that problems rarely occur. There are fundamental properties possessed by the well-known queueing models that eliminate classical rootfinding problems. Most importantly, we show that distinctness of roots is common within simply determined regions in the complex plane and provide conditions under which the characteristic equations for the G/EK/1 and EK/G/1 models have easily found, distinct roots. Furthermore, we show that the characteristic equation for the more general G/GEK/1 model has a collection of real and complex roots which are effectively distinct and located in clearly defined regions of the complex domain. Extensive computational results are given to support our contentions. INFORMS Journal on Computing, ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.

This paper examines a generalization of the exhaustive and one-at-a-time-discipline M/G/1 server vacation models. This alternative model is viewed as a state-dependent (nonvacation) M/G/1 queue in which the original service times are extended to include a (possibly zero length) state-dependent vacation after each service. Such a vacation policy permits greater flexibility in modeling real problems, and does, in fact, subsume most prior M/G/1 approaches. This device reveals a fundamental decomposition somewhat like that previously established for the classical vacation disciplines. In addition, necessary and sufficient conditions for system ergodicity are established for the state-dependent vacation policy, and some comments are offered on computations.

No abstract

We propose two related methods for deriving probability distribution estimates using approximate rational Laplace transform representations. Whatever method is used, the result is a Coxian estimate for an arbitrary distribution form or plain sample data, with the algebra of the Coxian often simplifying to a generalized hyperexponential or phase-type. The transform (or, alternatively, the moment-generating function) is used to facilitate the computations and leads to an attractive algorithm. For method one, the first 2N − 1 derivatives of the transform are matched with those of an approximate rational function; for the second method, a like number of values of the transform are matched with those of the approximation. The numerical process in both cases begins with an empirical Laplace transform or truncation of the actual transform, and then requires only the solution of a relatively small system of linear equations, followed by root finding for a low-degree polynomial. Besides the computationally attractive features of the overall procedure, it addresses the question of the number of terms, or the order, involved in a generalized hyperexponential, phase-type, or Coxian distribution, a problem not adequately treated by existing methods. Coxian distributions are commonly used in the modeling of single-stage and network queueing problems, inventory theory, and reliability analyses. They are particularly handy in the development of large-scale model approximations.