Two types of tasks are to be scheduled on a single processor under incomplete information about the task lengths. We derive the structure of optimal scheduling rules w.r.t. flowtime, as well as asymptotic approximations for a large number of tasks, when the length distributions belong to a one-parameter exponential family.

The asymptotic probability of buffer overflow for a queueing system with a Markov fluid input and deterministic service rate is derived by way of large deviation theory. The equations characterizing the deviant behavior are presented and examples are given for which closed-form solutions may be obtained. An independence result extends the analysis to cases where the input is an aggregate of independent Markov fluids.

We consider the following problem. For a given population of m items, we have to make a decision whether or not the population includes a relatively large cluster of identical items. This decision affects the effectiveness of a subsequent computational process, depending on the actual existence of the cluster and its size. To make a good decision, we use a statistical sample which should indicate the existence of a cluster and find a representative thereof. This paper describes the optimal sampling technique to be used in such a case, given the cost of the sampling and the potential gain in speed of the subsequent process. The optimal fixed sample size is specified, as well as the optimal sequential sampling, along with characterizing the dependence of the cost function on the truncation point.

For the case that the a priori distribution of the cluster proportion is known, we present formulae by which the optimal sampling procedures can be easily calculated. For the common situation in which the a priori distribution is not known, we present, in the case of a fixed sample size, a tight upper bound for the sample size, which is independent of the a priori distribution, and for the case of the sequential sampling, we present an approximately optimal truncation point, which is also independent of the a priori distribution.

The situation described arose in connection with choosing the best sorting method, an application that will be described in full detail. The most interesting practical result is that for our application truncating the sequential procedure at 35 observations, out of a population of 25,000–30,000 items, guarantees that in our sorting application we are always within 2.1% of the optimal cost independently of the a priori distribution.

We consider the problem of establishing the existence of stationary distributions for continuous-time Markov chains directly from the transition rates Q. Given an invariant probability distribution m for Q, we show that a necessary and sufficient condition for m to be a stationary distribution for the minimal process is that Q be regular. We provide sufficient conditions for the regularity of Q that are simple to verify in practice, thus allowing one to easily identify stationary distributions for a variety of models. To illustrate our results, we shall consider three classes of multidimensional Markov chains, namely, networks of queues with batch movements, semireversible queues, and partially balanced Markov processes.

The minimum expected number of binomial group tests is lower bounded by the cost of a particular Huffman coding problem whose solution is known. Thus, the information lower bound in binomial group testing is improved when the probability that each item is defective is small.

The two-dimensional correlated Wiener process (or Brownian motion) with drift is considered. The Fokker-Planck (or Kolmogorov forward) equation for the Wiener process (X1(t), X2(t)) is solved under absorbing boundary conditions on the lines x1= h1 and x2 = h2 and a fixed starting point (x0,1, x0,2). The first passage (or first exit) time when the process leaves the domain G = ( −∞, h1) × ( −∞, h2) is derived.

We consider an inventory system with compound Poisson demands replenished by discrete production of units on a single-server facility. This facility may start a vacation at any production completion epoch; at the completion of a vacation the inventory level is inspected to decide whether or not to resume production. Unit production and vacation times are independent and identically distributed with general distributions. Under an (s, S) policy, production is terminated when the inventory level reaches a critical level S, and production is resumed when the inventory level, upon completion of a vacation, is at or below a prespecified value s. In this paper we prove that an (s, S) policy is optimal among all possible policies. We also derive a highly efficient algorithm for the determination and evaluation of an optimal (s, S) policy.

Directed acyclic independence graphs (DAIGs) play an important role in recent developments in probabilistic expert systems and influence diagrams (Chyu [1]). The purpose of this note is to show that DAIGs can usefully be grouped into equivalence classes where the members of a single class share identical Markov properties. These equivalence classes can be identified via a simple graphical criterion. This result is particularly relevant to model selection procedures for DAIGs (see, e.g., Cooper and Herskovits [2] and Madigan and Raftery [4]) because it reduces the problem of searching among possible orientations of a given graph to that of searching among the equivalence classes.

Interest in the informational content of truncation motivates the study of the residual entropy function, that is, the entropy of a right truncated random variable as a function of the truncation point. In this note we show that, under mild regularity conditions, the residual entropy function characterizes the probability distribution. We also derive relationships among residual entropy, monotonicity of the failure rate, and stochastic dominance. Information theoretic measures of distances between distributions are also revisited from a similar perspective. In particular, we study the residual divergence between two positive random variables and investigate some of its monotonicity properties. The results are relevant to information theory, reliability theory, search problems, and experimental design.

Tasks belonging to N priority classes arrive for processing in a single or multiserver facility. If the processing does not begin by a certain time (deterministic or random), the task is lost and a cost is incurred. We determine properties of dynamic, nonidling, nonpreemptive strategies that minimize an infinite horizon expected cost.

The density of the first passage time in a nonstationary Gaussian process with random mean function can be approximated with arbitrary accuracy from a regression-type expansion. CROSSREG is a package of FORTRAN subroutines that perform intelligent transformations and numerical integrations to produce high-accuracy approximations with a minimum of computer time. The basic routines, collected in the unit ONEREG, give the density of the crossing time of a general bound. An additional set of routines make up the unit TWOREG, which also gives the bivariate density of the crossing time and the value of an accompanying process at the time of the crossing. These routines can be used to find the wavelength and amplitude density in any stationary Gaussian process. ONEREG and TWOREG are special cases of a routine MREG, which is the main routine in CROSSREG.

This paper proposes some new statistics based on k-NN distances for assessing the uniformity of a set of points belonging to a d-dimensional bounded region. Size and power of the tests have been estimated and compared to those of the modified Hopkins' statistic.

Let [NA(t), t≥0] and [NB(t), t≥0] be two counting processes with independent interarrivals Ai and Bi, respectively, i ∈ + ≡ [1,2,… ], and let [Xi, i ∈ +] and [Yi, i ∈ +] be two sequences of independent non-negative random variables. Consider the two compound processes [U(t), t ≥ 0] and [V(t), t ≥ 0] defined as U(t) = Xi, t ≥ 0, and V(t) = Yi, t ≥ 0. Then it is well known (and not hard to verify) that if the Ai's are independent of the Xi's, and if the Bi's are independent of the yi's, and if Ai ≥st Bi and Xi ≤st Yi, for all i ∈ +, then [U(t), t ≥ 0] ≤st [V(t), t ≥ 0]. In this paper we provide conditions under which this stochastic order relation holds even if Ai ≤st Bi and/or Xi ≥st Yi for all i ∈ +. In fact, we derive the stochastic order relationships among processes that are much more general than the cumulative ones described above. For example, it is not necessarily assumed that the Ai's are independent of the Xi's or that the Bi's are independent of the Yi's. Examples that illustrate the applications of the theory are included.

We consider a dam model with a compound Poisson input having positive jumps in which the release rule can be dynamically controlled between two release functions, r1 and r2. The dam process has two switchover levels, a and b, 0 < a < b < ∞, which are prescribed to switch the release rule from r2 to r1 only when the dam downcrosses levels and switch from r1 to r2 only when the dam upcrosses level b. We derive integral equations whose solutions determine the stationary distribution of the dam content and demonstrate such a determination explicitly for the case of exponential jumps. We also compute an expression for the average number of switches from r1 to r2, and vice versa; consider an approximation to the general case (with b − a being sufficiently large with respect to r1 and r2 and the jump sizes), which allows the use of asymptotic results from renewal theory; and study the wet period analysis for the case of exponential jumps.

In this paper the objective is to find a job sequence that minimizes, stochastically or in expectation, the sum of the total weighted job earliness and the total weighted job tardiness on one machine, when the job processing times are independent and identically distributed random variables. We first derive results for the case in which the jobs share a common, random due date. We then obtain results when the job due dates are distinct, independent random variables.

Let X be a continuous random variable denoting the lifetime of a unit. Let Xk:n denote the kth order statistic based on n independent random observations on X. It has been shown that if Xk:n has decreasing failure rate (DFR) for some k, 1 ≤ k ≤ n, then X is DFR. For n ≥ 2, if Xk:n has increasing failure rate (IFR), then Xk:n–1 is also IFR, and if Xk:n is DFR, then Xk:n+1 is also DFR. The log concavity of the density, function is shown to be preserved by the kth order statistic. It has been established that if the density function of Xk:n is log convex then the density function of Xk:n+1 is also log convex. Because a k-out-of-n system of i.i.d. components each having a life distribution that of X has lifetime Xn-k+1:n the results have applications in the study of such systems.