Nonstochastic 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 beneficial. Popular methodologies include queuing networks, Markov chains, simulation techniques, and discrete event systems (such as Petri nets). These methodologies are primarily stochastic. Performance evaluation mandates results that are probabilistic in nature (such as the average rate of part deliveries) and relies on probabilistic inputs (such as the probability of breakdown or the distributions associated with a manufacturing process). This paper examines non-stochastic analysis, which can be useful for verifying correct operation. Manufacturing systems are modeled using timed-event graphs, which are similar to decision-free Petri nets augmented with timing information, and an example demonstrates the efficacy of nonstochastic analysis.