Integration of scheduling and dynamic optimization of batch processes under uncertainty

Yunfei Chu, Fengqi You*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish (US)
Title of host publication2014 American Control Conference, ACC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4997-5002
Number of pages6
ISBN (Print)9781479932726
DOIs
StatePublished - Jan 1 2014
Event2014 American Control Conference, ACC 2014 - Portland, OR, United States
Duration: Jun 4 2014Jun 6 2014

Other

Other2014 American Control Conference, ACC 2014
CountryUnited States
CityPortland, OR
Period6/4/146/6/14

Keywords

  • Manufacturing systems
  • Optimization
  • Uncertain systems

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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