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AbstractDespite the general acceptance of exponential smoothing, the choice of a specific smoothing model is often a difficult problem. Previous research involving smoothing‐model comparisons and the penalties for selection of the wrong model has been limited. This paper evaluates the performance of a representative group of smoothing models over a variety of conditions in 9,000 simulated time series. Forecast‐error results demonstrate that a major disadvantage of adaptive smoothing models is their tendency to generate unstable forecasts, even during periods when mean demand itself is stable. Several trend‐adjusted smoothing models are shown to be robust forecasters, whether the time series actually display a trend or not.

AbstractProposed is a Heuristic Network (HN) Procedure for balancing assembly lines. The procedure uses simple heuristic rules to generate a network which is then traversed using a shortest route algorithm to obtain a heuristic solution. The advantages of the HN Procedure are: a) it generally yields better solutions than those obtained by application of the heuristics, and b) sensitivity analysis with different values of cycle time is possible without having to regenerate the network. The rationale for its effectiveness and its application to problems with paralleling are presented. Computational experience with the procedure on up to 50 task test problems is provided.