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.
- Yao-Hsin Chou, Yu-Chi Jiang, and Shu-Yu Kuo. 2021. Portfolio Optimization in Both Long and Short Selling Trading Using Trend Ratios and Quantum-Inspired Evolutionary Algorithms. IEEE Access 9 (2021), 152115--152130. Google ScholarCross Ref
- C. A. C. Coello. 2006. Evolutionary multi-objective optimization: a historical view of the field. IEEE computational intelligence magazine 1 (2006), 28--36.Google Scholar
- J. Das, I.; Dennis. 1998. Normal-boundary intersection: A new method for generating the Pareto surface in nonlinear multicriteria optimization problems. SIAM J. Optimization 8 (1998), 631--657.Google ScholarDigital Library
- H. Deb, K.; Jain. 2014. An Evolutionary Many-Objective Optimization Algorithm Using Reference-point Based Non-dominated Sorting Approach, Part I: Solving Problems with Box Constraints. IEEE Transactions on Evolutionary Computation 18 (2014), 577--601.Google ScholarCross Ref
- K. Deb. 2001. Multi-objective Optimization using Evolutionary Algorithms (1st ed.). John Wiley & Sons, England.Google Scholar
- K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan. 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6 (2002), 182--197.Google ScholarDigital Library
- A. V. Deursen and P. Klint. 2002. Domain Specific Language Design Requires Feature Descriptions. Journal of Computing and Information Technology 10, n.1 (2002), 1--17.Google Scholar
- D. M. Dias. 2010. Programação Genética Linear com Inspiração Quântica. Tese de Doutorado. Pontifícia Universidade Católica do Rio de Janeiro, Rio de Janeiro.Google Scholar
- E. M. N. Figueiredo, T. B. Ludermir, and C. J. A. Bastos Filho. 2016. Many Objective Particle Swarm Optimization. Information Science 53 (2016), 1689--1700.Google Scholar
- K. Jain, H.;Deb. 2014. An evolutionary many-objective optimization algorithm using reference-point based nondominated sorting approach, Part II: Handling constraints and extending to an adaptive approach. IEEE Transactions on Evolutionary Computation 18 (2014), 602--622.Google ScholarCross Ref
- K. Li, K. Deb, Q. Zhang, and S. Kwong. 2015. An Evolutionary Many-Objective Optimization Algorithm Based on Dominance and Decomposition. IEEE Transactions on Evolutionary Computation 19 (5) (2015), 694--716.Google Scholar
- A. Masood, Y. Mei, G. Chen, and M. Zhang. 2016. Many-Objective Genetic Programming for Job-Shop Scheduling. IEEE Congress on Evolutionary Computation 63 (2016), 219--216.Google Scholar
- J. A. M. Berenguer. 2014. Optmización Evolutiva Multiobjetivo basada en el Algoritmo de Kuhn-Munkres. Dissertação de Mestrado. Centro de investigación y de estudios avanzados del instituto politécnico nacional, México.Google Scholar
- A. Mohammadi, M. N. Omidvar, X. Li, and K. Deb. 2014. Integrating user preferences and decomposition methods for many-objective optimization. IEEE Congress on Evolutionary Computation (CEC) (2014), 421--428.Google Scholar
- Oscar H. Montiel Ross. 2020. A Review of Quantum-Inspired Metaheuristics: Going From Classical Computers to Real Quantum Computers. IEEE Access 8 (2020), 814--838. Google ScholarCross Ref
- L. F. L. Moro. 2000. Técnicas de Otimização Mista-Inteira para o Planejamento e Programação de produção em Refinarias de Petróleo. Tese de Doutorado. Escola Politécnica, Universidade de São Paulo, São Paulo.Google Scholar
- S. M. S. Neiro, V. V. Murata, B. Jahn, R. R. Seixas, E. H. Hollmann, and C. S. Pereira. 2019. Dealing with Multiple Tank Outflows and In-Line Blending in Continuous-Time Crude Oil Scheduling Problems. Industrial Engineering Chemistry Research 58 (2019), 4495--4510. Issue 11.Google ScholarCross Ref
- F. Oliveira, P. Nunes, R. Blajberg, and S. Hamacher. 2016. A framework for crude oil scheduling in an integrated terminal-refinery system under supply uncertainty. European Journal of Operational Research 252 (2016), 635--645.Google ScholarCross Ref
- A. Oulasvirta, J. Hukkinen, and B. Schwartz. 2009. When more is Less: the paradox of choice in search engine use. Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval (2009), 516--523.Google ScholarDigital Library
- C. S. Pereira, D. M. Dias, M. A. C. Pacheco, M. M. B. R. Vellasco, A. V. Abs Da Cruz, and E. H. Hollmann. 2020. Quantum-Inspired Genetic Programming Algorithm for the Crude Oil Scheduling of a Real-World Refinery. IEEE Systems Journal (2020), 1--12.Google Scholar
- C. S. Pereira, D. M. Dias, M. B. R. Vellasco, F. H. F. Viana, and L. Marti. 2018. Crude oil refinery scheduling: addressing a real world multiobjective problem through genetic programming and dominance-based approach. Proceedings of the Genetic and Evolutionary Computation Conference (2018), 1821--1828.Google Scholar
- Y. Qi, X. Ma, F. Liu, L. Jiao, J. Sun, and J. Wu. 2014. MOEA/D with Adaptative Weight Adjustment. Evolutionary Computation 22 (2014), 221--248.Google ScholarDigital Library
- D. Panda; M. Ranteke. 2019. Preventive crude oil scheduling under demand uncertainty using structure adapted genetic algorithm. Applied Energy 235 (2019), 68--82.Google ScholarCross Ref
- Hatem M. H. Saad, Ripon K. Chakrabortty, Saber Elsayed, and Michael J. Ryan. 2021. Quantum-Inspired Genetic Algorithm for Resource-Constrained Project-Scheduling. IEEE Access 9 (2021), 38488--38502. Google ScholarCross Ref
- Weishi Shao, Zhongshi Shao, and Dechang Pi. 2021. An Ant Colony Optimization Behavior-Based MOEA/D for Distributed Heterogeneous Hybrid Flow Shop Scheduling Problem Under Nonidentical Time-of-Use Electricity Tariffs. IEEE Transactions on Automation Science and Engineering (2021), 1--16. Google ScholarCross Ref
- A. Trivedi, D. Srinivasan, K. Sanyal, and A. Ghosh. 2017. A Survey of Multiobjective Evolutionary Algorithms based on Decomposition. Applied Soft Computing Journal 21 (3) (2017), 440--462.Google Scholar
- Yao Wang, Zhiming Dong, Tenghui Hu, and Xianpeng Wang. 2020. An Improved MOEA/D Algorithm for the Carbon Black Production Line Static and Dynamic Multiobjective Scheduling Problem. In 2020 IEEE Congress on Evolutionary Computation (CEC). 1--8. Google ScholarDigital Library
- Z. Wang, Q. Zhang, M. Gong, and A. Zhou. 2014. A replacement strategy for balancing convergence and diversity in MOEA/D. Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014 (2014), 2132--2139.Google Scholar
- J. Xu, S. Zhang, J. Zhang, S. Wang, and Q. Xu. 2017. Simultaneous scheduling of front-end crude transfer and refinery processing. Computers and Chemical Engineering 96 (2017), 212--236.Google ScholarCross Ref
- H. Zhang, Q.; Li. 2007. MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition. IEEE Transactions on Evolutionary Computation 11 (2007), 712--731.Google ScholarDigital Library
- Q. Zhang and H. Li. 2009. Multiobjective Optimization Problems With Complicated Pareto Sets, MOEA/D and NSGA-II. IEEE Transactions on Evolutionary Computation 13 (2009), 284--302.Google ScholarDigital Library
- E. Zitzler and K. Simon. 2004. Indicator-Based Selection in Multiobjective Search. 8th International Conference on Parallel Problem Solving from Nature (PPSN VIII) i (2004), 832--842.Google Scholar
Index Terms
- A Multi-Objective Decomposition Optimization Method for Refinery Crude Oil Scheduling through Genetic Programming
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