Aprimoramento de um modelo evolutivo multiobjetivo aplicado ao problema de sequenciamento de bateladas na manufatura de produtos farmacêuticos
Resumo
A otimização do Sequenciamento de Bateladas (SB) em problemas reais de manufatura farmacêutica é desafiadora devido aos múltiplos objetivos conflitantes, restrições e demanda incerta. Um dos desafios é a baixa convergência de soluções viáveis. Algoritmos Genéticos Multiobjetivos (AGMO) podem ser utilizados para lidar com esse desafio. Propomos aprimoramentos nos operadores genéticos de mutação e cruzamento, bem como uma nova estratégia de inicialização da população, visando aprimorar a qualidade das soluções em relação a métricas como Hipervolume (Hv), Distância Geracional Invertida mais (IGD+), Taxa de Erro (E), Cobertura entre dois conjuntos de soluções não-dominadas (CS) e Número de Soluções válidas (NSV). Os resultados demonstram que as melhorias propostas reduzem a mediana do IGD+ em 76,6% e o E em 12,1%, enquanto aumentam o NSV em 25,0%.
Palavras-chave:
Manufatura Farmacêutica, Algoritmos Genéticos Multiobjetivo, Otimização Multiobjetivo Restrita
Referências
Bertsimas, D. and Tsitsiklis, J. N. (1997). Introduction to linear optimization, volume 6.
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.
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.
Publicado
25/09/2023
Como Citar
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: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (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.
