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We present a two-phase approach to design and analysis of manufacturing cells based on simulated experimentation and response surface methodology using a general manufacturing-cell simulation model. The first phase involves factor-screening simulation experiments to identity design and operational factors that have a significant effect on cell performance as measured by a comprehensive annual cost function. In the second phase of experimentation, we construct simulation (response surface) metamodels to describe the relationship between the significant cell design and operational factors ( the controllable input parameters) and the resulting simulation-based estimate of expected annual cell cost (the output response). We use canonical and ridge analyses of the estimated response surface to estimate the levels of the quantitative input factors that minimize the cell's expected annual cost. We apply this methodology to an assembly cell for printed circuit hoards. Compared to be current cell operating policy. the simulation metamodel-based estimate of the optimum operating policy is predicted to yield average annual savings of approximately $425 000, which is a 20% reduction in annual cost. In a companion paper, we detail the structure and operation of the manufacturing-cell simulation model. [ABSTRACT FROM AUTHOR]

In this paper, we study the effects of an imperfect production process on the optimal production cycle time. The system is assumed to deteriorate during the production process and produce some proportion of defective items. The optimal production cycle is derived, and is shown to be shorter than that of the classical Economic Manufacturing Quantity model. The analysis is extended to the case where the defective rate is a function of the set-up cost, for which the set-up cost level and the production cycle time are jointly optimized. Finally, we also consider the case where the deterioration process is dynamic in its nature, i.e., the proportion of defective items is not constant. Both linear, exponential, and multi-state deteriorating processes are studied. Numerical examples are provided to illustrate the derivation of the optimal production cycle time in these situations. [ABSTRACT FROM AUTHOR]

Two alternatives to the multivariate exponentially weighted moving average (EWMA) chart are considered. One of these alternatives is an arithmetic moving average control chart which is the arithmetic average of the sample means for the last k periods. The other alternative is a truncated version of the EWMA which truncates the EWMA after a fairly short period of time so that more emphasis is placed on the most current observation. Simulated average run length (ARL) results indicate that for some situations these alternatives charts outperform the multivariate EWMA chart. Some suggestions are made for designing charts to detect a specific shift and comparing the alternative charts. Some authors have noted that past in-control data may diminish the chart's ability to detect a shift in the process mean. To examine this, the scenario will be discussed when the process is in-control initially but goes out-of-control at some random time period. This is more like a realistic manufacturing setting, where the process is in-control initially, but after some time the process mean shifts to a new mean and in this paper it will be shown which control charts detect a shift faster using this scenario. [ABSTRACT FROM AUTHOR]

This paper proposes a technique for estimating steady-state quantiles from discrete-event simulation models, with particular attention paid to cycle time quantiles of manufacturing systems. The technique is based on the Cornish-Fisher expansion, justified through an extensive empirical study, and is supported with mathematical analysis. It is shown that the technique provides precise and accurate estimates for the most commonly estimated quantiles with minimal data storage and low computational requirements. [ABSTRACT FROM AUTHOR]