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A multi-objective decomposition optimization method for refinery crude oil scheduling through genetic programming
Date
2023
Abstract
This paper proposes an evolutionary algorithm integrating genetic programming and a decomposition-based multi-objective algorithm to address a crude oil refinery scheduling problem. Four objectives are modelled, two related to maintaining the crude oil processing level, and the other two aim to keep the refinery operations as smooth as possible. The proposed method, Constrained-Decomposition of Quantum-Inspired Grammar-based Linear Genetic Programming (C-DQIGLGP), uses Quantum-Inspired Grammar-based Linear Genetic Programming (QIGLGP), replacing its hierarchical approach for the objectives with a multi-objective decomposition-based one. To achieve this goal, QIGLGP was profoundly modified regarding sorting individuals, updating the population, and applying the evolutionary operator. Individuals whose objective values related to processing level are under a predefined limit are better ranked. We compare the results of C-DQIGLGP for five scenarios from a real refinery to those obtained by QIGLGP and Constrained Non-dominated Sort QIGLGP (C-NSQIGLGP), from literature, demonstrating the better performance of C-DQIGLGP for all cases.
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Description
Publisher
Accounting for Computing Machinery
Citation
GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation, pp. 1972-1980
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Mota_Dias_2023_Multi.pdf
Adobe PDF, 8.39 MB
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Funding Information
We thank PETROBRAS, CNPq, CAPES, and FAPERJ for sponsoring this study and for providing the information and support needed for its development. The third author was funded in part by CORFO/ ANID International Centers of Excellence Program 10 CEII-9157 Inria Chile.