Cycle Time Throughput Research Articles

Neural network metamodeling for cycle time-throughput profiles in manufacturing

This paper proposed a neural network (NN) metamodeling method to generate the cycle time (CT)–throughput (TH) profiles for single/multi-product manufacturing environments. Such CT–TH profiles illustrate the trade-off relationship between CT and TH, the two critical performance measures, and hence provide a comprehensive performance evaluation of a manufacturing system. The proposed methods distinct from the existing NN metamodeling work in three major aspects: First, instead of treating an NN as a black box, the geometry of NN is examined and utilized; second, a progressive model-fitting strategy is developed to obtain the simplest-structured NN that is adequate to capture the CT–TH relationship; third, an experiment design method, particularly suitable to NN modeling, is developed to sequentially collect simulation data for the efficient estimation of the NN models.

Estimating Cycle Time Percentile Curves for Manufacturing Systems via Simulation

Cycle time-throughput (CT-TH) percentile curves quantify the relationship between percentiles of cycle time and factory throughput, and they can play an important role in strategic planning for manufacturing systems. In this paper, a highly flexible distribution, the generalized gamma, is used to represent the underlying distribution of cycle time. To obtain CT-TH percentile curves, we use a factory simulation to fit metamodels for the first three CT-TH moment curves throughout the throughput range of interest, determine the parameters of the generalized gamma by matching moments, and obtain any percentile of interest by inverting the distribution. To insure efficiency and control estimation error, simulation experiments are built up sequentially using a multistage procedure. Numerical results are presented to demonstrate the effectiveness of the approach.

Variance-based sampling for simulating cycle time—throughput curves using simulation-based estimates

Simulation at several different traffic intensities is required when generating a cycle time-throughput (CT-TH) curve. Previous work has shown that a variance-based allocation using an asymptotic variance approximation results in more precise confidence intervals than those achieved through naïve sampling in which an equal amount of effort is allocated to each traffic intensity being investigated. Many systems for which these CT-TH curves are desired are too complex for an asymptotic variance approximation to be easily determined. This paper presents a fixed-budget variance-based sampling allocation procedure for the simulation of CT-TH curves using variance estimates of the sample mean calculated from pilot simulation runs. The proposed allocation procedure significantly improved the range of precision over the naïve allocation.

Efficient generation of cycle time‐throughput curves through simulation and metamodeling

AbstractA cycle time‐throughput (CT‐TH) curve, which quantifies the relationship of long‐run average cycle time to throughput rate, plays an important role in strategic planning for manufacturing systems. In this paper, a nonlinear regression metamodel supported by queueing theory is developed to represent the underlying CT‐TH curve implied by a manufacturing simulation model. To estimate the model efficiently, simulation experiments are built up sequentially using a multistage procedure. Extensive numerical experiments are presented to demonstrate the effectiveness of the proposed procedure. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2007

A three-phase simulation methodology for generating accurate and precise cycle time throughput curves

Cycle time-throughput (CT-TH) curves allow a visual examination of the relationship between projected average cycle time and throughput. They permit one to appraise sensitivity of cycle time to start rate through curvature or steepness. This paper presents an overall framework for efficiently generating simulation-based CT-TH curves that are both accurate and precise. The framework presented in this paper consists of three phases: • fixed sample allocation methods • prioritisation of design point inclusion • run length determination through sequential stopping rules. The first phase proposes how to efficiently generate a precise and accurate CT-TH curve given fixed sample size limitations. The second phase provides a set of sequentially ranked design points for systematic simulation experimentation. Finally, the third phase provides a sequential stopping rule that is developed to determine the length of a simulation run based on a time series forecasting procedure. A case study is presented which illustrates the methodology and highlights the fact that the technique provides workable guidelines for both experienced and novice simulation practitioners faced with the task of generating an accurate and precise cycle time-throughput curve. The case study illustrates that this framework is able to produce an equivalently accurate and precise CT-TH curve, for a five station M/M/1 queuing model at a cost of 25% of the samples required using the naive approach.