A material handling system modeling framework: a data-driven approach for the generation of discrete-event simulation models

The design and reconfiguration of Material Handling Systems (MHSs) at the factory scale are known to be complex. Various design and reconfiguration alternatives have to be considered and evaluated through indicators such as: On Time Delivery (OTD) within the plant, number of material shortages or product waiting time, etc. Due to the dynamic behavior of MHS, simulation-based approaches play an essential role in such analysis. However, developing simulation models for MHS can be time-consuming (especially for modeling Large Scale Systems) and difficult to build (some skills and knowledge are required to use simulation software). To overcome these challenges, data-driven approaches have been proposed in the literature for the generation of MHS simulation models. Nevertheless, the available approaches focus on specific domains and may not always account for all the necessary data, including MHS control policies. Therefore, this paper aims to propose a framework that employs a data catalog regrouping five data categories (layout, product features, production process, material handling process, and MHS control methods) to support the generation of MHS simulation models using SIMIO. The article details the data structure used to gather MHS simulation data, the selection of a simulation tool, the modeling patterns integrated into the simulations, and the application of the transformation rules. The whole approach is implemented to form the generation framework. The framework is designed to assist decision-makers (who have basic simulation knowledge) in the evaluation of MHS design/reconfiguration alternatives. The paper finally presents a validation of the framework on two case studies.