Simulation for manufacturing system design and operation: Literature review and analysis

By Ashkan Negahban, Jeffrey S. Smith. 2014

567 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Digital twin-driven rapid individualised designing of automated flow-shop manufacturing system (2019, methodology): “The proposed DT-driven RID model is a hybrid approach integrating discrete event models (Negahban and Smith 2014) with system dynamic models (Shardaa and Banerjee 2013) to evaluate design decisions in AFMS.”
Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook (2020, unknown): “Sub-stantial amounts of research have been devoted to the analysis of manufacturing system dynamics and performance [15 – 19].”
Human–Cyber–Physical Systems (HCPSs) in the Context of New-Generation Intelligent Manufacturing (2019, background): “0; however, such a method presents the following major challenges: (1) In big data intelligent modeling, how can industrial big data be acquired and managed with greater efficiency? How can the effective learning of knowledge in big data be enabled? How can the ability to solve uncertain and complex”
Health-aware hierarchical control for smart manufacturing using reinforcement learning (2017, methodology): “Constructing a simulation of a manufacturing systems allows for the evaluation of different system configuration and operating strategies to support decision making [33].”