This paper develops a model for allocating cross-trained workers at the beginning of a shift in a multidepartment service environment. It assumes departments are trying to maximize objective functions that are concave with respect to the number of workers assigned. Worker capabilities are described by parameters that range from zero to one, with fractional values representing workers who are less than fully qualified. The nonlinear programming model presented is a variant of the generalized assignment problem. The model is used in a series of experiments to investigate the value of cross-utilization as a function of factors such as demand variability and levels of cross-training. Results show that the benefits of cross-utilization can be substantial, and in many cases a small degree of cross-training can capture most of the benefits. Beyond a certain amount additional cross-training adds little additional value, and the preferred amount depends heavily on the level of demand variability.manpower scheduling, service operations management, mathematical programming

We investigate the small-sample behavior and convergence properties of confidence interval estimators (CIEs) for the mean of a stationary discrete process. We consider CIEs arising from nonoverlapping batch means, overlapping batch means, and standardized time series, all of which are commonly used in discrete-event simulation. The performance measures of interest are the coverage probability, and the expected value and variance of the half-length. We use empirical and analytical methods to make detailed comparisons regarding the behavior of the CIEs for a variety of stochastic processes. All the CIEs under study are asymptotically valid; however, they are usually invalid for small sample sizes. We find that for small samples, the bias of the variance parameter estimator figures significantly in CIE coverage performance—the less bias the better. A secondary role is played by the marginal distribution of the stationary process. We also point out that some CIEs require fewer observations before manifesting the properties for CIE validity.

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We describe two simulation models for repair processes of aircraft in the Navy, and suggest ways to reduce cycle time and improve readiness. The models illustrate the effects of material availability and process redesign on repair cycle time and work-in-process inventory levels for critical components. Our results indicate that the Navy could significantly reduce repair cycle times of those components by increasing stock levels of relatively inexpensive repair parts and slightly modifying current repair processes.

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Abstract:We propose three related estimators for the variance parameter arising from a steady-state simulation process. All are based on combinations of standardized-time-series area and Cramér-von Mises (CvM) estimators. The first is a straightforward linear combination of the area and CvM estimators; the second resembles a Durbin-Watson statistic; and the third is related to a jackknifed version of the first. The main derivations yield analytical expressions for the bias and variance of the new estimators. These results show that the new estimators often perform better than the pure area, pure CvM, and benchmark nonoverlapping and overlapping batch means estimators, especially in terms of variance and mean squared error. We also give exact and Monte Carlo examples illustrating our findings.

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