753 research outputs found

    M/G/ oo with Batch Arrivals

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    OR 169-87 Revised Version. Original copy, WP1932-87 September 1987, also enclosed was submitted to the University of Rochester.Let po(n) be the distribution of the number N(oo) in the system at ergodicity for systems with an infinite number of servers, batch arrivals with general batch size distribution and general holding times. This distribution is of impotance to a variety of studies in congestion theory, inventory theory and storage systems. To obtain this distribution, a more general problem is addressed . In this problem, each epoch of a Poisson process gives rise to an independent stochastic function on the lattice of integers, which may be viewed as a stochastic impulse response. A continuum analogue to the lattice process is also provided

    The bivariate maximum process and quasi-stationary structure of birth-death processes

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    AbstractLet N(t) be a birth-death process on {0,1,…} with state 0 reflecting and let qTK be the quasi-stationary distribution of the truncated process on {0,1,…, K} with λK > 0. It is shown that the sequence (qTK) increases stochastically with K. The bivariate Markov chain (M(t), N(t)) where M(t)=max0≤t′≤tN(t′) is studied as a stepping stone to the proof of the result

    On exponential ergodicity and spectral structure for birth-death processes, II

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    AbstractIn Part I, Feller's boundary theory was described with simple conditions for process classification. The implications of this boundary classification scheme for spectral structure and exponential ergodicity are examined in Part II. Conditions under which the spectral span is finite or infinite are established. A time-dependent norm is exhibited describing the exponentiality of the convergence and its uniformity. Specific systems are discussed in detail: Contents:1.7. Spectral structure for the M/M/I process2.8. Exponential ergodicity for processes with entrance, exit, and regular boundaries3.9. Exponential ergodicity for processes with natural boundaries4.10. Uniformity of exponential convergence5.11. Finite and Infinite spectral span6.12. Skip-free processes on the full lattic

    Families of infinitely divisible distributions closed under mixing and convolution

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    Certain families of probability distribution functions maintain their infinite divisibility under repeated mixing and convolution. Examples on the continuum and lattice are given. The main tools used are Polya's criteria and the properties of log-convexity and complete monotonicity. Some light is shed on the relationship between these two properties

    The Compensation Method Applied to a One-Product Production Inventory Problem

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    This paper considers a one-product, one-machine production/inventory probelm. Demand requests for the product are governed by a Poisson process with demand size being an exponential random variable. The production facility may be in production or idle; while in production, the facility produces continuously at a constant rate. The objective is to minimize system costs consisting of setup costs, inventory holding costs, and backorder costs. Given a two-critical-number policy, the problem is analyzed as a constrained Markov process using the compensation method. The policy space may then be searched to find the optimal policy.Research supported, in part, by the Office of Naval Research under Contract N00014-75-C-0556

    Markov chains and M-matrices: Inequalities and equalities

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    Extended Vacation Systems and the Universality of the M/G/1/K Blocking Formula

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    A simple blocking formula B(K) = (1 - p)EK [1 - pEK]- 1 relates the probability of blocking for the finite capacity M/G/1/K to EK, the steady state occupancy tail probability of the same system with infinite capacity. The validity of this formula is demonstrated for M/G/1 vacation systems augmented by an idle state, an umbrella for a host of priority systems and vacation systems related to M/G/1. A class of occupancy level dependent vacation systems introduced are shown to require a variant of this blocking formula

    Networks of Non-homogeneous M/G/oo Systems

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    For a network of G/oo service facilities, the transient joint distribution of the facility populations is found to have a simple Poisson product form with simple explicit formula for the means. In the network it is assumed that: a) each facility has an infinite number of servers; b) the service time distributions are general; c) external traffic is non-homogenous in time; d) arrivals have random or deterministic routes through the network possibly returning to the same facility more than once; e) arrivals use the facilities on their route sequentially or in parallel (as in the case of a circuit switched telecommunication network). The results have relevance to communication networks and manufacturing systems
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