Generative Modeling of Holonic Manufacturing Execution Systems for Batch Plants

Abstract

Most batch plants are constantly subjected to unplanned disruptive events such as arrivals of rush orders, raw material delays or equipment breakdowns along with a multitude of interactions in the supply chain demanding automatic on-line rescheduling and execution control. For responsiveness and agility at the shop-floor, a holonic batch control architecture based on autonomic units is proposed. A generative modeling of the proposed holonic manufacturing execution system (MES) is presented in order to evaluate its emerging behavior and macroscopic dynamics in a multiproduct batch plant. A simulation model of the proposed holonic MES for a case study was implemented in Netlogo. Different scenarios are considered to assess disturbance rejection capability by the holonic MES. Results obtained are very promising.