Integrated planning, scheduling, and dynamic optimization for continuous processes. Shi, H., Chu, Y., & You, F. In Proceedings of the IEEE Conference on Decision and Control, volume 2015-Febru, 2014.
abstract   bibtex   
? 2014 IEEE.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.
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 title = {Integrated planning, scheduling, and dynamic optimization for continuous processes},
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 year = {2014},
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 abstract = {? 2014 IEEE.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.},
 bibtype = {inProceedings},
 author = {Shi, H. and Chu, Y. and You, F.},
 booktitle = {Proceedings of the IEEE Conference on Decision and Control}
}

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