Abstract
Efficient algorithms are essential for reliable lead time forecasting as they are often the basis for subsequent activities such as planned order releases and shop scheduling. Dynamic conditions are prevalent in modern manufacturing systems and reliable lead time forecasts can provide a competitive edge operationally. In this paper, we explore the use of efficient algorithms for steady state analysis to develop approximate estimates of lead times under dynamic conditions.
| Original language | English (US) |
|---|---|
| Title of host publication | 2017 13th IEEE Conference on Automation Science and Engineering, CASE 2017 |
| Publisher | IEEE Computer Society |
| Pages | 994-999 |
| Number of pages | 6 |
| Volume | 2017-August |
| ISBN (Electronic) | 9781509067800 |
| DOIs | |
| State | Published - Jan 12 2018 |
| Event | 13th IEEE Conference on Automation Science and Engineering, CASE 2017 - Xi'an, China Duration: Aug 20 2017 → Aug 23 2017 |
Other
| Other | 13th IEEE Conference on Automation Science and Engineering, CASE 2017 |
|---|---|
| Country | China |
| City | Xi'an |
| Period | 8/20/17 → 8/23/17 |
Keywords
- Dynamic Demand
- Lead Time Forecasting
- Queueing Networks
ASJC Scopus subject areas
- Control and Systems Engineering
- Electrical and Electronic Engineering
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Cite this
Askin, R., & Hanumantha, G. J. (2018). Automated lead time estimation for manufacturing networks with dynamic demand. In 2017 13th IEEE Conference on Automation Science and Engineering, CASE 2017 (Vol. 2017-August, pp. 994-999). IEEE Computer Society. https://doi.org/10.1109/COASE.2017.8256232