Modeling and Performance Evaluation of Inventory Systems Using Batch Deterministic and Stochastic Petri Nets

This paper presents our work on modeling and performance analysis of inventory systems using batch deterministic and stochastic Petri nets (BDSPNs). It addresses issues frequently raised by industrial companies, but did not receive enough attention by the Petri nets (PNs) community in spite of its important role in the study of discrete event systems. The BDSPN is a new class of PNs capable of describing the synchronization of discrete and batch token flows in discrete batch processes. Such processes appear in inventory systems or more general supply chains where materials are purchased in finite discrete quantities (batches of different sizes), and many operations such as inventory replenishment and customer order fulfillment are usually performed in a batch way because of the batch nature of customer orders and/or in order to take advantages of the economies of scale. In this paper, the BDSPN model is formally introduced, and its conflict resolutions of transitions and batch firing indexes are addressed. The model is then applied to the modeling and performance evaluation of various inventory systems. Analytic performance evaluation techniques are developed for the model with illustrative applications to the inventory systems. Our study shows that the model is powerful for both modeling and performance evaluation of the systems.