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SysML Models for Discrete Event Logistics Systems

Published

Author(s)

Timothy A. Sprock, Conrad E. Bock

Abstract

System models and model-based engineering methods have the promise of transforming the way that industrial engineers interact with production and logistics systems. Model-based methods play a role in improving communication between stakeholders, interoperability between systems, automated access to consistent analysis models, and multi-disciplinary design methods for complex systems. However, there remains a need for a foundation for modeling these kinds of systems -- a foundation that tailors methods and tools developed in other engineering domains to the unique concepts and semantics of production and logistics. This foundation is the topic of these models. This repository contains model libraries for modeling discrete event logistics systems (DELS), an abstraction that covers manufacturing plants, material handling and transportation systems, warehouses, supply chains, etc. The DELS abstraction was created by identifying and modeling commonalities across the kinds of systems that industrial engineers typically encounter, and analysis models they use to analyze those system. It extends well-known product, process, and resource (PPR) ontologies to incorporate a library of operational control model components, and is connected to Commodity Flow Network (CFN), modeling networks, flow networks, and process networks. The relationship between DELS and CFN formally links system models to abstractions used to create analysis models, such as discrete event simulation.
Citation
Journal of Research (NIST JRES) -
Volume
125

Keywords

Industrial Engineering, Smart Manufacturing, SysML, System Modeling

Citation

Sprock, T. and Bock, C. (2020), SysML Models for Discrete Event Logistics Systems, Journal of Research (NIST JRES), National Institute of Standards and Technology, Gaithersburg, MD, [online], https://doi.org/10.6028/jres.125.023 (Accessed November 20, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created July 28, 2020, Updated March 1, 2021