This paper studies the problem of scheduling jobs on parallel machines with setup times. When a machine switches from processing one type of job to another type, setup times are incurred. The problem is to find a feasible schedule for each machine which maximizes the total reward. We study three heuristics for solving this problem. Analytical and empirical results of the heuristics are given.

We focus on ways of combining simple database models with managerial intuition. We present a model and method for isolating managerial intuition. For five different business forecasting situations, our results indicate that a combination of model and manager always outperforms either of these decision inputs in isolation, an average R 2 increase of 0.09 (16%) above the best single decision input in cross-validated model analyses. We assess the validity of an equal weighting heuristic, 50% model + 50% manager, and then discuss why our results might differ from previous research on expert judgment.

The transactional data of a queueing system are the recorded times of service commencement and service completion for each customer served. With increasing use of computers to aid or even perform service one often has machine readable transactional data, but virtually no information about the queue itself. In this paper we propose a way to deduce the queueing behavior of Poisson arrival queueing systems from only the transactional data and the Poisson assumption. For each congestion period in which queues may form (in front of a single or multiple servers), the key quantities obtained are mean wait in queue, time-dependent mean number in queue, and probability distribution of the number in queue observed by a randomly arriving customer. The methodology builds on arguments of order statistics and usually requires a computer to evaluate a recursive function. The results are exact for a homogeneous Poisson arrival process (with unknown parameter) and approximately correct for a slowly time varying Poisson process.

Clemen and Winkler (1985) have described the theoretical effectiveness of Winkler's (1981) formula for optimally combining forecasts. The optimality of Winkler's formula is, however, contingent on actually knowing the forecasters' statistical properties, i.e., the variances and covariances of their forecasts. In realistic applications, of course, these properties have to be estimated, usually from a set of prior forecasts. In this case we show how the “operationally optimal” combining strategy differs from Winkler's “theoretically optimal” formula. Specifically, we provide figures indicating the operationally optimal strategy for combining two forecasts. We then propose a heuristic to choose the best set of parameter estimates in combining any number of forecasters and demonstrate its effectiveness via simulation.

Securities whose payoffs depend upon the actual path of the underlying state variables pose problems for standard backward-valuation techniques. In this paper, we show that there is a method of solving such problems that requires the addition of but a single auxiliary state variable. The technique is illustrated by examining an adjustable rate mortgage (ARM) with yearly and lifetime caps. The ARM amortizes and can be prepaid at any time. We also handle other prominent features of ARMs including points and teasers. This method of using a single auxiliary state variable to handle path dependencies is a general one, applicable to more complex models of the economic environment and to other financial contracts.

New point and interval estimators for quantiles that employ a control variate are introduced. The properties of these estimators do not depend on the usual assumption of joint normality between the random variable of interest and the control. Illustrative examples for queueing and stochastic activity network models are given. In those examples, the new estimators are superior to the standard estimator in terms of the mean squared error of the point estimator and the length of the confidence interval.