We present an exact solution method for a single-server queueing system which alternates between periods in which service can be provided (on-periods) and periods in which the server is out of operation (off-periods). The arrival process is Poisson, on-periods are assumed to have a phase-type distribution, and service times and off-periods are assumed to be arbitrary.
We consider the polymatroidal flow network model which incorporates two important extensions of the standard maximal flow problem: general concave objective functions of the vector of supplies to a collection of sinks, as well as polymatroidal capacity restrictions on sets of arcs emanating from or pointing to a common node. A number of important applications are reviewed.
Often the timing of certain activities has a strong periodic element. Due to circumstances an activity is sometimes made outside the regular cycle, but it does not break the cycle. Thus, the timing of future activities is highly predictable. We provide a stochastic model where the data are not seasonal yet the optimal behaviour has a strong periodic element.
We acknowledge that the behavior of the OSHA and EPA is complex and cannot be explained by simple capture theories, we nonetheless find ample evidence of OSHA and EPA actions that unnecessarily exacerbate or even artificially create indirect effects for political purposes (what we call enforcement asymmetries). Furthermore, despite mounting evidence of the inefficiency of OSHA and EPA, Congress has continued to be uninterested in adequate monitoring of regulatory effect, much less in regulatory reform.
We consider general queueing models dealing with multiple classes of customers and address the question under what conditions and in what (stochastic) sense the marginal increase in various performance measures, resulting from the addition of a new class of customers to an existing system, is larger than if the same class were added to a system dealing with only a subset of its current customer base.
Heuristic solution methods for combinatorial optimization problems are often based on local neighborhood searches. These tend to get trapped in a local optimum and the final result is often heavily dependent on the starting solution. Simulated annealing methods attempt to avoid these problems by randomizing the procedure so as to allow for occasional changes that worsen the solution. In this paper we provide probabilistic analyses of different designs of these methods.
The authors propose a likelihood-ratio test of the hypothesis that the minimum-variance frontier of a set of K assets coincides with the frontier of this set and another set of N assets. They study the relation between this hypothesis, exact arbitrage pricing, and mutual fund separation. The exact distribution of the test statistic is available. The authors test the hypothesis that the frontier spanned by three size-sorted stock portfolios is the same as the frontier spanned by thirty-three size-sorted stock portfolios.
We consider a class of loss systems with exponential service times and a Poisson arrival process with a rate that varies periodically among N levels called seasons. For two special cases, we derive transient and steady-state solutions and provide simple proofs that losses are minimized when the arrival rates for all seasons are equal. In the general case, we describe a straightforward procedure to derive the steady-state probabilities. We also prove that when S=1, the server is generally busier during the high arrival rate seasons.