Statistical tolerance intervals are widely used in the industry and in various areas of sciences, especially in conformity assessment and acceptance of products or processes in terms of quality. When the interest is in precision, a tolerance interval for the variance is useful. In this paper, we consider two‐sided tolerance intervals for the population of sample variances for data that arise from a normal distribution. These intervals are useful in applications where one needs information about process deterioration as well as process improvement, to properly assess product quality. In this paper, the theory for these tolerance intervals is developed and tables for the tolerance factors, required to calculate the proposed tolerance limits, are provided for various settings. Construction and implementation of the proposed tolerance intervals are illustrated using a dataset from a real application. Summary and conclusions are offered.

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This paper describes a study of the dispatch planning/scheduling process for inbound containers handled with a reach stacker. Client container pickup is scheduled at least one day in advance for one of six two-hour time windows (six five-container-high stacks per time window) on a given day. A buffer area is available for the containers to be moved in when clients are being served. The aim of this study was to determine the conditions required to ensure that all the containers are dispatched within the scheduled time window and so meet the clients' requirements. To this end, the performance indicators were identified and compared using simulations as an analytical tool. The results indicate that the shortest-processing-time (SPT) queueing discipline is preferable to the first-come-first-served (FCFS) discipline and that client arrivals can usefully be restricted to periods shorter than two hours in order to meet container-dispatch and service-quality objectives.