Abstract
Integration of scheduling and dynamic optimization significantly improves the overall performance of a production process compared to the traditional sequential method. However, most integrated methods focus on solving deterministic problems without explicitly taking process uncertainty into account. We propose a novel integrated method for sequential batch processes under uncertainty. The integrated problem is formulated into a two-stage stochastic program. To solve the resulting complicated integrated problem, we develop an efficient algorithm based on the framework of generalized Benders decomposition. For a complicated case study with more than 3 million variables/equations under 100 scenarios, the direct solution approach does not find a feasible solution while the decomposition algorithm return the optimal solution in 23.9 hours. The integrated method returns a higher average profit than the sequential method by 17.6%.
Original language | English (US) |
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Title of host publication | 2014 American Control Conference, ACC 2014 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 4997-5002 |
Number of pages | 6 |
ISBN (Print) | 9781479932726 |
DOIs | |
State | Published - Jan 1 2014 |
Event | 2014 American Control Conference, ACC 2014 - Portland, OR, United States Duration: Jun 4 2014 → Jun 6 2014 |
Other
Other | 2014 American Control Conference, ACC 2014 |
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Country | United States |
City | Portland, OR |
Period | 6/4/14 → 6/6/14 |
Keywords
- Manufacturing systems
- Optimization
- Uncertain systems
ASJC Scopus subject areas
- Electrical and Electronic Engineering