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In dynamic, stochastic manufacturing systems, production planners and manufacturing engineers can benefit from understanding how rescheduling strategies affect system performance. This knowledge will help these experts design and operate better manufacturing planning and control systems. This paper presents new analytical models that can predict the performance of rescheduling strategies and quantify the trade-offs between different performance measures. In the parallel machine systems under consideration, jobs of different types arrive dynamically, and setups occur when production changes from one job type to another. Three rescheduling strategies are studied: periodic, hybrid, and event-driven based on the queue size. The scheduling algorithm groups jobs of the same type in batches to eliminate unnecessary setups. The analytical models require less computational effort than simulation models, and experimental results show that they accurately estimate important performance measures like average flow time, machine utilization, and setup frequency.

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This paper investigates inference and volatility forecasting using a Markov switching heteroscedastic model with a fat-tailed error distribution to analyze asymmetric effects on both the conditional mean and conditional volatility of financial time series. The motivation for extending the Markov switching GARCH model, previously developed to capture mean asymmetry, is that the switching variable, assumed to be a first-order Markov process, is unobserved. The proposed model extends this work to incorporate Markov switching in the mean and variance simultaneously. Parameter estimation and inference are performed in a Bayesian framework via a Markov chain Monte Carlo scheme. We compare competing models using Bayesian forecasting in a comparative value-at-risk study. The proposed methods are illustrated using both simulations and eight international stock market return series. The results generally favor the proposed double Markov switching GARCH model with an exogenous variable. Copyright © 2008 John Wiley & Sons, Ltd.