Name
Abstract | ||
|---|---|---|
Computer based discrete event simulation (DES) is one of the most commonly used aids for the design of automotive manufacturing systems. However, DES tools represent machines in extensive detail, while only representing workers as simple resources. This presents a problem when modelling systems with a highly manual work content, such as an assembly line. This paper describes research at Cranfield University, in collaboration with the Ford Motor Company, founded on the assumption that human variation is the cause of a large percentage of the disparity between simulation predictions and real world performance. The research aims to improve the accuracy and reliability of simulation prediction by including models of human factors. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1016/S1569-190X(03)00094-7 | Simulation Modelling Practice and Theory |
Keywords | Field | DocType |
Manufacturing simulation,Human performance,Micro-models | Industrial engineering,Computer science,Control engineering,Artificial intelligence,Machine learning,Discrete event simulation,Automotive manufacturing,Work content | Journal |
Volume | Issue | ISSN |
12 | 7 | 1569-190X |
Citations | PageRank | References |
22 | 1.80 | 3 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tim Baines | 1 | 23 | 2.21 |
| Stephen Mason | 2 | 22 | 1.80 |
| Peer-Olaf Siebers | 3 | 186 | 27.03 |
| John Ladbrook | 4 | 99 | 11.16 |