Abstract
Multi-product systems with finite buffers and sequence-dependent set-up times are quite common in modern manufacturing industry. In practice, the distribution of machine processing time could be arbitrary, while in existing literature it is often assumed to follow an exponential distribution. In this paper, we develop an analytical method to study the multi-product manufacturing systems with non-exponential processing times. An embedded Markov chain model is constructed and two approximation methods, Gamma estimation and linear approximation, are proposed. The model is validated with high accuracy by numerical experiments and practical data from an automotive assembly system. © 2014 Taylor & Francis.
| Original language | English |
|---|---|
| Pages (from-to) | 983-1001 |
| Journal | International Journal of Production Research |
| Volume | 53 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 1 Feb 2015 |
| Externally published | Yes |
Bibliographical note
Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].Funding
This study is supported in part by Sichuan Changhong Electric Co., Ltd., and by NSF [grant number CMMI-1114263].
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 9 Industry, Innovation, and Infrastructure
Research Keywords
- Manufacturing systems
- Markov processes
- Non-exponential processing time
- Throughput
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