NONSTOCHASTIC ANALYSIS OF MANUFACTURING SYSTEMS USING TIMED-EVENT GRAPHS

Using automated methods to analyze the temporal behavior of manufactur ing systems has proven to be essential and quite beneficial. Popular m ethodologies include queuing networks, Markov chains, simulation techn iques, and discrete event systems (such as Petri nets). These methodol ogies are primarily stochastic. Performance evaluation mandates result s that are probabilistic in nature (such as the average rate of part d eliveries) and relies on probabilistic inputs (such as the probability of breakdown or the distributions associated with a manufacturing pro cess). This paper examines nonstochastic analysis, which can be useful for verifying correct operation. Manufacturing systems are modeled us ing timed-event graphs, which are similar to decision-free Petri nets augmented with timing information, and an example demonstrates the eff icacy of nonstochastic analysis.