TY - GEN
T1 - A Multi-Objective Decomposition Optimization Method for Refinery Crude Oil Scheduling through Genetic Programming
AU - Pereira, Cristiane Salgado
AU - Martí, Luis
AU - Dias, Douglas Mota
AU - Vellasco, Marley
N1 - Publisher Copyright:
© 2023 Copyright held by the owner/author(s).
PY - 2023/7/15
Y1 - 2023/7/15
N2 - 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.
AB - 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.
KW - decomposition
KW - evolutionary multi-objective optimization
KW - genetic programming
KW - quantum-inspired algorithm
KW - refinery scheduling
UR - https://www.scopus.com/pages/publications/85168994476
U2 - 10.1145/3583133.3596313
DO - 10.1145/3583133.3596313
M3 - Conference contribution
AN - SCOPUS:85168994476
T3 - GECCO 2023 Companion - Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion
SP - 1972
EP - 1980
BT - GECCO 2023 Companion - Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion
PB - Association for Computing Machinery, Inc
T2 - 2023 Genetic and Evolutionary Computation Conference Companion, GECCO 2023 Companion
Y2 - 15 July 2023 through 19 July 2023
ER -