Integrated planning, scheduling, and dynamic optimization for continuous processes

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.

@inProceedings{
 title = {Integrated planning, scheduling, and dynamic optimization for continuous processes},
 type = {inProceedings},
 year = {2014},
 identifiers = {[object Object]},
 volume = {2015-Febru},
 issue = {February},
 id = {da5361a9-ea42-35ad-8699-281d8dd75654},
 created = {2016-08-19T13:20:37.000Z},
 file_attached = {false},
 profile_id = {f27171f8-b200-3870-9028-dcb6a084733d},
 last_modified = {2017-03-25T02:01:24.507Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 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}
}

Downloads: 0

{"_id":"KfE9aQ6Y9yx4Y9izn","bibbaseid":"shi-chu-you-integratedplanningschedulinganddynamicoptimizationforcontinuousprocesses-2014","downloads":0,"creationDate":"2017-12-28T02:43:51.801Z","title":"Integrated planning, scheduling, and dynamic optimization for continuous processes","author_short":["Shi, H.","Chu, Y.","You, F."],"year":2014,"bibtype":"inProceedings","biburl":null,"bibdata":{"title":"Integrated planning, scheduling, and dynamic optimization for continuous processes","type":"inProceedings","year":"2014","identifiers":"[object Object]","volume":"2015-Febru","issue":"February","id":"da5361a9-ea42-35ad-8699-281d8dd75654","created":"2016-08-19T13:20:37.000Z","file_attached":false,"profile_id":"f27171f8-b200-3870-9028-dcb6a084733d","last_modified":"2017-03-25T02:01:24.507Z","read":false,"starred":false,"authored":"true","confirmed":false,"hidden":false,"private_publication":false,"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","bibtex":"@inProceedings{\n title = {Integrated planning, scheduling, and dynamic optimization for continuous processes},\n type = {inProceedings},\n year = {2014},\n identifiers = {[object Object]},\n volume = {2015-Febru},\n issue = {February},\n id = {da5361a9-ea42-35ad-8699-281d8dd75654},\n created = {2016-08-19T13:20:37.000Z},\n file_attached = {false},\n profile_id = {f27171f8-b200-3870-9028-dcb6a084733d},\n last_modified = {2017-03-25T02:01:24.507Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n 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.},\n bibtype = {inProceedings},\n author = {Shi, H. and Chu, Y. and You, F.},\n booktitle = {Proceedings of the IEEE Conference on Decision and Control}\n}","author_short":["Shi, H.","Chu, Y.","You, F."],"bibbaseid":"shi-chu-you-integratedplanningschedulinganddynamicoptimizationforcontinuousprocesses-2014","role":"author","urls":{},"downloads":0},"search_terms":["integrated","planning","scheduling","dynamic","optimization","continuous","processes","shi","chu","you"],"keywords":[],"authorIDs":[]}