The Journal of Bigdata (한국빅데이터학회지)

Korea Bigdata Society (한국빅데이터학회)
권호 표지
DOI QR코드

DOI QR Code

A Genetic Algorithm for Production Scheduling of Biopharmaceutical Contract Manufacturing Products

바이오의약품 위탁생산 일정계획 수립을 위한 유전자 알고리즘

  • Ji-Hoon Kim ;
  • Jeong-Hyun Kim ;
  • Jae-Gon Kim
  • 김지훈 (인천대학교 산업경영공학과) ;
  • 김정현 (인천대학교 산업경영공학과) ;
  • 김재곤 (인천대학교 산업경영공학과)
  • Received : 2024.05.30
  • Accepted : 2024.06.11
  • Published : 2024.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

In the biopharmaceutical contract manufacturing organization (CMO) business, establishing a production schedule that satisfies the due date for various customer orders is crucial for competitiveness. In a CMO process, each order consists of multiple batches that can be allocated to multiple production lines in small batch units for parallel production. This study proposes a meta-heuristic algorithm to establish a scheduling plan that minimizes the total delivery delay of orders in a CMO process with identical parallel machine. Inspired by biological evolution, the proposed algorithm generates random data structures similar to chromosomes to solve specific problems and effectively explores various solutions through operations such as crossover and mutation. Based on real-world data provided by a domestic CMO company, computer experiments were conducted to verify that the proposed algorithm produces superior scheduling plans compared to expert algorithms used by the company and commercial optimization packages, within a reasonable computation time.

바이오의약품 위탁생산 사업(CMO)에서 다양한 고객의 주문에 대해 납기를 만족시키는 생산 일정계획을 수립하는 것은 사업이 경쟁력 측면에서 매우 중요하다. CMO 공정에서 각 주문은 다수의 배치로 구성되어 있으며 복수 개의 생산라인에 소량의 배치 단위로 할당되어 병렬 생산할 수 있다. 본 연구는 동종 병렬설비를 보유하고 있는 CMO 공정에서 주문의 총 납기 지연을 최소화하는 일정계획을 수립하기 위한 메타휴리스틱 알고리즘을 제안한다. 제안된 알고리즘은 생물학적 진화에서 영감을 받아 염색체와 같은 데이터 구조를 무작위로 생성하여 특정 문제를 해결하며, 교차 및 돌연변이와 같은 작업을 사용하여 다양한 솔루션을 효과적으로 탐색한다. 국내 CMO 기업체에서 제공한 현업 데이터를 기반으로 컴퓨터 실험을 진행하여 제안한 알고리즘이 기업체에서 사용하고 있는 전문가 알고리즘과 상용 최적화 패키지보다 우수한 일정계획을 적절한 계산시간 내에 도출하는 것을 확인하였다.

Keywords

Acknowledgement

This work was supported by Incheon National University Research Grant in 2024.

References

  1. Harjunkoski, I., C. T. Maravelias, P. Bongers, P. M. Castro, S. Engell, I. E. Grossmann, ... and J. Wassick, "Scope for industrial applications of production scheduling models and solution methods," Computers & Chemical Engineering, Vol. 62, pp. 161-193, 2014.
  2. Pandya, E. J. and K. V. Shah, "CONTRACT MANUFACTURING IN PHARMA INDUSTRY," Pharma Science Monitor, Vol. 4, No. 3, pp. 123-144, 2013.
  3. Rinnooy Kan, A. H. G., "Machine scheduling problems: classification, complexity and computations," Springer Science & Business Media, 2012.
  4. Lenstra, J. K., and A. H. G. Rinnooy Kan, "Complexity of scheduling under precedence constraints," Operations Research, Vol.26, No.1, pp.22-35, 1978.
  5. Garey, M. R. and D. S. Johnson, "A Guide to the Theory of NP-Completeness," Computers and intractability pp.37-79, 1990.
  6. Tahar, D. N., F. Yalaoui, C. Chu, and L. Amodeo, "A linear programming approach for identical parallel machine scheduling with job splitting and sequence-dependent setup times," International journal of production economics Vol.99, No.(1-2), pp.63-73, 2006.
  7. Eroglu, D. Y. and H. C. Ozmutlu, "Mip models and hybrid algorithms for simultaneous job splitting and scheduling on unrelated parallel machines," The Scientific World Journal 2014, 2014.
  8. Tutumlu, B., and T. Sarac, "A MIP model and a hybrid genetic algorithm for flexible job-shop scheduling problem with job-splitting," Computers & Operations Research, Vol. 155, pp.106222, 2023.
  9. Avgerinos, I., I. Mourtos, S. Vatikiotis, and G. Zois, "Scheduling unrelated machines with job splitting, setup resources and sequence dependency," International Journal of Production Research Vol.61, No.16, pp. 5502-5524, 2023.
  10. Liu, C., "A Hybrid Genetic Algorithm to Minimize Total Tardiness for Unrelated Parallel Machine Scheduling with Precedence Constraints," Mathematical Problems in Engineering, pp.537127, 2013.
  11. Holand, J. H., "Adaptation in Natural and Artificial Systems," Ann Harbor: University of Michigan Press, 1975.
  12. Kumar, M., D. M. Husain, N. Upreti, and D. Gupta, "Genetic algorithm: Review and application," Available at SSRN, Vol.2, No.2, pp.451- 454, 2010.
  13. Munoz, A. and F. Rubio, "Evaluating genetic algorithms through the approximability hierarchy," Journal of Computational Science, Vol. 53, 101388, 2021.
  14. Lakhdar, K., J. Savery, L. G. Papageorgiou and S. S. Farid, "Multiobjective long-term planning of biopharmaceutical manufacturing facilities," Biotechnology progress, Vol.23, No.6, pp.1383-1393, 2007.
  15. Oyebolu, F. B., J. van Lidth de Jeude, C. Siganporia, ... and J. Branke, "A new lot sizing and scheduling heuristic for multi-site biopharmaceutical production," Journal of Heuristics, Vol.23, pp.231-256, 2017.
  16. Sahling, F. and G. J. Hahn, "Dynamic lot sizing in biopharmaceutical manufacturing," International Journal of Production Economics, Vol.207(C), pp.96-106, 2019.
  17. Kim, Y. D., S. O. Shim, S. B. Kim, Y. C. Choi and H. M. Yoon, "Parallel machine scheduling considering a job-splitting property," International Journal of Production Research, Vol.42, No.21, pp.4531-4546, 2004.
  18. Logendran, R. and F. Subur, "Unrelated parallel machine scheduling with job splitting," IIE Transactions Vol.36, No.4, 359-372, 2004.
  19. Eroglu, Y. D., H. C. Ozmutlu, and S. Ozmutlu, "Genetic algorithm with local search for the unrelated parallel machine scheduling problem with sequence-dependent set-up times," International Journal of Production Research Vol.52, No.19, pp.5841-5856, 2014.
  20. Kim, J. G., S. W. Song, and B. J. Jeong, "Minimizing total tardiness for the identical parallel machine scheduling problem with splitting jobs and sequence-dependent setup times," International Journal of Production Research, Vol.58, No.6, pp.1628-1643, 2020.
  21. Lee, J. H., H. Jang, and H. J. Kim, "Iterative job splitting algorithms for parallel machine scheduling with job splitting and setup resource constraints," Journal of the Operational Research Society Vol.72, No.4, pp.780-799, 2021.
  22. Zheng, F., K. Jin, Y. Xu and M. Liu, "Unrelated parallel machine scheduling with processing cost, machine eligibility and order splitting," Computers & Industrial Engineering, Vol.171, pp.108483, 2022.
  23. 심재민, "바이오의약품 제조공정에서의 생산계획 최적화 모델," 국내 석사 학위 논문 인천대학교 공학대학원, 2024.
  24. Xing, W. and J. Zhang, "Parallel machine scheduling with splitting jobs," Discrete Applied Mathematics, Vol.103, No.(1-3), pp.259-269, 2000.
  25. Bean, J. C., "Genetic algorithms and random keys for sequencing and optimization," ORSA Journal of Computing, Vol. 6, No. 2, pp. 154-160, 1994.
  26. De Andrade, C. E., R. F. Toso, M. G. Resende and F. K. Miyazawa, "Biased random-key genetic algorithms for the winner determination problem in combinatorial auctions," Evolutionary computation, Vol.23, No.2, pp.279-307, 2015.
  27. Goncalves, J. F. and M. G. Resende, "Biased random-key genetic algorithms for combinatorial optimization," Journal of Heuristics, Vol. 17, No. 5, pp.487-525, 2011.