Integrated planning, scheduling, and dynamic optimization for continuous processes

Hanyu Shi, Yunfei Chu, Fengqi You*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Integration of planning, scheduling, and dynamic optimization significantly improves the overall performance of a production process, compared to the traditional sequential method that solves each sub-problem one by one. The integrated model can be formulated as a mixed-integer dynamic optimization (MIDO) problem which can be then transformed into a mixed-integer nonlinear program (MINLP). However, widely-used simultaneous methods, which solve the integrated problem by a general-purpose MINLP solver, encounter computational complexity. They are difficult to apply to large-scale problems. To address this difficulty, we propose a novel efficient method to solve the integrated problem for a multi-product reactor. The method decomposes the dynamic optimization problems from the planning and scheduling problem by discretizing transition times and transition costs. Then the integrated problem is transformed into a mixed-integer linear program, which is much easier to solve than the large-scale MINLP. In the case studies, the proposed method can reduce the computational time by more than three orders of magnitudes in comparison with the simultaneous method.

Original languageEnglish (US)
Article number7039412
Pages (from-to)388-393
Number of pages6
JournalProceedings of the IEEE Conference on Decision and Control
Volume2015-February
Issue numberFebruary
DOIs
StatePublished - Jan 1 2014

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

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