Aprimoramento de um modelo evolutivo multiobjetivo aplicado ao problema de sequenciamento de bateladas na manufatura de produtos farmacêuticos
Abstract
The optimization of batch sequencing in real pharmaceutical manufacturing problems is challenging due to conflicting multiple objectives, constraints, and uncertain demand. One of the challenges is the low convergence of feasible solutions, which can be addressed by using Multi-Objective Genetic Algorithms. We propose improvements to the genetic operators of mutation and crossover, as well as a new population initialization strategy, aiming to enhance the quality of solutions in terms of Hipervolume (Hv), Inverted Generational Distance plus (IGD+), Error Rate (E), Coverage between two sets of non-dominated solutions (CV), and Number of Valid Solutions (NSV). The results demonstrate that the proposed enhancements reduce IGD+ by 71.9% and E by 11.4%, while increasing NSV by 24.9%.
Keywords:
Pharmaceutical Manufacturing, Multiobjective Evolutionary Algorithms, Constrained Multiobjective Optimization
References
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Ishibuchi, H., Masuda, H., Tanigaki, Y., and Nojima, Y. (2015). Modified distance calculation in generational distance and inverted generational distance. Evol. Multi-criterion Optimization, pages 110–125.
Jankauskas, K. and Farid, S. S. (2019). Multi-objective biopharma capacity planning under uncertainty using a flexible genetic algorithm approach. Comput. and Chemical Eng., 128:35–52.
Jia, Z.-H., Gao, L.-Y., and Zhang, X.-Y. (2020). A new history-guided multi-objective evolutionary algorithm based on decomposition for batching scheduling. Expert Systems With Applications, 141.
Li, Z., Qian, B., Hu, R., Chang, L., and Yang, J. (2019). An elitist nondominated sorting hybrid algorithm for multi-objective flexible job-shop scheduling problem with sequence-dependent setups. Knowledge-Based Systems, 173:83–112.
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Sand, G., Till, J., Tometzki, T., Urselmann, M., Engell, S., and Emmerich, M. (2008). Engineered versus standard evolutionary algorithms: A case study in batch scheduling with recourse. Comput. and Chemical Eng., 32:2706–2722.
Zhang, B., ke Pan, Q., lei Meng, L., Lu, C., hui Mou, J., and qing Li, J. (2022). An automatic multi-objective evolutionary algorithm for the hybrid flowshop scheduling problem with consistent sublots. Knowledge-Based Systems, 238:107819.
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Zitzler, E. and Thiele, L. (1999). Multiobjective evol. algorithms: A comparative case study and the strength pareto approach. IEEE Trans. Evol. Comput., 3.
Bezdan, T., Zivkovic, M., Bacanin, N., Strumberger, I., Tuba, E., and Tuba, M. (2022). Multi-objective task scheduling in cloud computing environment by hybridized bat algorithm. J. of Intelligent and Fuzzy Systems, 42:411–423.
Cott, B. J. and Macchietto, S. (1989). Minimizing the effects of batch process variability using online schedule modification. Comput. and chemical engineering, 13:105–113.
Deb, K. (1999). Multi-objective genetic algorithms: problem difficulties and construction of test problems. Evol. computation, 7:205–230.
Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans. Evol. Comput., 6:182–197.
Fonseca, C. M. and Fleming, P. J. (1998). Multiobjective optimization and multiple constraint handling with evolutionary algorithms part i: A unified formulation. IEEE Trans. Systems, Man, and Cybernetics Part A:Systems and Humans., 28:26–37.
Fu, Y., Zhou, M., Guo, X., and Qi, L. (2021). Stochastic multi-objective integrated disassembly-reprocessing-reassembly scheduling via fruit fly optimization algorithm. J. of Cleaner Production, 278:123364.
Gao, K., Cao, Z., Zhang, L., Chen, Z., Han, Y., and Pan, Q. (2019). A review on swarm intelligence and evolutionary algorithms for solving flexible job shop scheduling problems. IEEE/CAA J. of Automatica Sinica, 6:904–916.
Honkomp, S., Lombard, S., and Pekny, J. (2000). Eng. the curse of reality-why process scheduling optimization problems are difficult in practice. Comput. and Chemical Comput. and Chemical Eng., 24.
Honkomp, S. J., Mockus, L., and Reklaitis, G. V. (1999). A framework for schedule evaluation with processing uncertainty. Comput. and Chemical Eng., 23:595–609.
Ishibuchi, H., Masuda, H., Tanigaki, Y., and Nojima, Y. (2015). Modified distance calculation in generational distance and inverted generational distance. Evol. Multi-criterion Optimization, pages 110–125.
Jankauskas, K. and Farid, S. S. (2019). Multi-objective biopharma capacity planning under uncertainty using a flexible genetic algorithm approach. Comput. and Chemical Eng., 128:35–52.
Jia, Z.-H., Gao, L.-Y., and Zhang, X.-Y. (2020). A new history-guided multi-objective evolutionary algorithm based on decomposition for batching scheduling. Expert Systems With Applications, 141.
Li, Z., Qian, B., Hu, R., Chang, L., and Yang, J. (2019). An elitist nondominated sorting hybrid algorithm for multi-objective flexible job-shop scheduling problem with sequence-dependent setups. Knowledge-Based Systems, 173:83–112.
Mann, H. B. and Whitney, D. R. (1947). On a test of whether one of two random variables is stochastically larger than the other. The annals of mathematical statistics, pages 50–60.
Mooney, C. Z. (1997). Monte carlo simulation.
Oyebolu, F. B. (2019). Meta-heuristic and hyper-heuristic scheduling tools for biopharmaceutical production.
Sand, G., Till, J., Tometzki, T., Urselmann, M., Engell, S., and Emmerich, M. (2008). Engineered versus standard evolutionary algorithms: A case study in batch scheduling with recourse. Comput. and Chemical Eng., 32:2706–2722.
Zhang, B., ke Pan, Q., lei Meng, L., Lu, C., hui Mou, J., and qing Li, J. (2022). An automatic multi-objective evolutionary algorithm for the hybrid flowshop scheduling problem with consistent sublots. Knowledge-Based Systems, 238:107819.
Zhou, S., Li, X., Du, N., Pang, Y., and Chen, H. (2018). A multi-objective differential evolution algorithm for parallel batch processing machine scheduling considering electricity consumption cost. Comput. and Operations Research, 96:55–68.
Zitzler, E. and Thiele, L. (1998). An evolutionary algorithm for multiobjective optimization: The strength pareto approach. TIK-report, 43.
Zitzler, E. and Thiele, L. (1999). Multiobjective evol. algorithms: A comparative case study and the strength pareto approach. IEEE Trans. Evol. Comput., 3.
Published
2023-09-25
How to Cite
KOHARA, Débora Toshie; OLIVEIRA, Gina Maira Barbosa de; MARTINS, Luiz Gustavo Almeida.
Aprimoramento de um modelo evolutivo multiobjetivo aplicado ao problema de sequenciamento de bateladas na manufatura de produtos farmacêuticos. In: NATIONAL MEETING ON ARTIFICIAL AND COMPUTATIONAL INTELLIGENCE (ENIAC), 20. , 2023, Belo Horizonte/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 1099-1113.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2023.234618.
