Simple bounds and monotonicity results for finite multi-server exponential tandem queues

Simple and computationally attractive lower and upper bounds are presented for the call congestion such as those representing multi-server loss or delay stations. Numerical computations indicate a potential usefulness of the bounds for quick engineering purposes. The bounds correspond to product-form modifications and are intuitively appealing. A formal proof of the bounds and related mono tonicity results will be presented. The technique of this proof, which is based on Markov reward theory, is of interest in itself and seems promising for further application. The extension to the non-exponential case is discussed. For multi-server loss stations the bounds are argued to be insensitive. Keywords: Tandem queues, product form, call congestion, bounds, monotonicity results, Markov chains

Simple bounds and monotonicity results for finite multi-server exponential tandem queues

Simple and computationally attractive lower and upper bounds are presented for the call congestion such as those representing multi-server loss or delay stations. Numerical computations indicate a potential usefulness of the bounds for quick engineering purposes. The bounds correspond to product-form modifications and are intuitively appealing. A formal proof of the bounds and related monotonicity results will be presented. The technique of this proof, which is based on Markov reward theory, is of interest in itself and seems promising for further application. The extension to the non-exponential case is discussed. For multiserver loss stations the bounds are argued to be insensitive

Comparing Markov Chains: Aggregation and Precedence Relations Applied to Sets of States, with Applications to Assemble-to-Order Systems

International audienceSolving Markov chains is, in general, difficult if the state space of the chain is very large (or infinite) and lacking a simple repeating structure. One alternative to solving such chains is to construct models that are simple to analyze and provide bounds for a reward function of interest. We present a new bounding method for Markov chains inspired by Markov reward theory: Our method constructs bounds by redirecting selected sets of transitions, facilitating an intuitive interpretation of the modifications of the original system. We show that our method is compatible with strong aggregation of Markov chains; thus we can obtain bounds for an initial chain by analyzing a much smaller chain. We illustrate our method by using it to prove monotonicity results and bounds for assemble-to-order systems