Non-stochastic Analysis of Manufacturing Systems using Timed Event Graphs

Using automated methods to analyze the temporal behavior of manufacturing systems has proven to be essential and quite bene-cial. Popular methodologies include Queueing networks, Markov chains, simulation techniques, and discrete event systems (such as Petri nets). These methodologies are primarily stochastic. Performance evaluation mandates results which are probabilistic in nature (such as the average rate of part deliveries) and relies on probabilis-tic inputs (such as the probability of breakdown, or the distributions associated with a manufacturing process). This paper examines non-stochastic analysis, which we argue can be useful for verifying correct operation. We model manufacturing systems using timed event graphs which are similar to decision free Petri nets augmented with timing information, and present an example that demonstrates the eecacy of non-stochastic analysis. Note: This paper was typeset and editted and appeared in a slightly mod