Optimal Order Scheduling for Corrugated Box Production on Flexographic Printing Machines Using a Genetic Algorithm
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Keywords
Production Scheduling, Genetic algorithm, flexographic printing, sequence-dependent setup times, color changeover
Abstract
In a flexographic printing machine, color changeover actually takes up excessive time and serves as a bottleneck in the corrugated cardboard packaging manufacturer. This causes inefficiency and reduces the output. The objective of this study is to optimize production scheduling using a Genetic Algorithm (GA) to minimize sequence-dependent setup times (SDST). The current manual scheduling method used in the studied flexographic printing environment does not explicitly account for the sequence-dependent color changeover structure considered in this study, creating opportunities for improved scheduling performance through optimized production sequencing. To address this problem, the SDST problem was first formulated as a Mixed-Integer Linear Programming (MILP) model based on systematic observation and historical production data. A customized GA was then developed to generate high-quality scheduling solutions, and a systematic parameter tuning process was conducted, identifying an effective configuration of population size 500, 100 generations, and mutation rate 0.7 to ensure stable convergence. Results show that the proposed GA framework reduced setups times by over 70.78% (equivalent to 5.04 hours per shift) compared to the facility’s existing manual scheduling baseline and also outperformed the greedy heuristic benchmark, consistently achieving an average setup time of 8,466.63 seconds across multiple runs with low variability, demonstrating reliable performance. This study shows that GA can serve as a practical approach for optimizing scheduling in flexographic printing and closely related sequence-dependent color changeover production contexts, although the current model is based on deterministic conditions with fixed job sequences, which may limit responsiveness to dynamic production uncertainties such as machine breakdowns or rush orders, suggesting the need for future enhancements using simulation and multi-objective optimization approaches.
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References
[2] N. C. Saha, “Food Packaging: Concepts and Its Significance,” 2022. doi: 10.1007/978-981-16-4233-3_1.
[3] Rajkumar and V. Jain, “A Literature Study on the Product Packaging Influences on the Customers Behavior,” Journal of Contemporary Issues in Business and Government| Vol, vol. 27, no. 3, p. 780, 2021.
[4] D. G. Ertaş and B. T. Dönmezer, “The Effect of Industrial Design on Corrugated Cardboard Packaging Optimization,” Archives of Design Research, vol. 38, no. 2, pp. 45–69, 2025.
[5] R. Fitas, H. J. Schaffrath, and S. Schabel, “A review of optimization for corrugated boards,” Sustainability, vol. 15, no. 21, p. 15588, 2023.
[6] T. Garbowski and M. Pośpiech, “The impact of scientific research on corrugated board on the lifecycle of packaging,” Packaging Review, 2024.
[7] T. Gajewski, T. Garbowski, N. Staszak, and M. Kuca, “Crushing of double-walled corrugated board and its influence on the load capacity of various boxes,” Energies (Basel), vol. 14, no. 14, p. 4321, 2021.
[8] T. Garbowski, T. Gajewski, and J. K. Grabski, “Estimation of the compressive strength of corrugated cardboard boxes with various perforations,” Energies (Basel), vol. 14, no. 4, p. 1095, 2021.
[9] K. R. Patel, “Harmonizing sustainability, functionality, and cost: navigating responsible packaging innovations in modern supply chains,” American Journal of Economic and Management Business (AJEMB), vol. 2, no. 8, pp. 287–300, 2023.
[10] T. Fadiji and P. B. Pathare, “Technological advancements in food processing and packaging,” 2023, MDPI.
[11] Z. Abusaq et al., “Improving energy performance in flexographic printing process through lean and AI techniques: a case study,” Energies (Basel), vol. 16, no. 4, p. 1972, 2023.
[12] A. P. Burger, C. G. Jacobs, J. H. van Vuuren, and S. E. Visagie, “Scheduling multi-colour print jobs with sequence-dependent setup times,” Journal of Scheduling, vol. 18, no. 2, 2015, doi: 10.1007/s10951-014-0400-2.
[13]A. Naderi, M. Zandieh, and S. M. T. Fatemi Ghomi, “Scheduling sequence-dependent setup time job shops with preventive maintenance,” International Journal of Advanced Manufacturing Technology, vol. 43, no. 1–2, 2009, doi: 10.1007/s00170-008-1693-0.
[14] Adhi, B. Santosa, and N. Siswanto, “A meta-heuristic method for solving scheduling problem: Crow search algorithm,” in IOP Conference Series: Materials Science and Engineering, 2018. doi: 10.1088/1757-899X/337/1/012003.
[15] Agnetis, J.-C. Billaut, M. Pinedo, and D. Shabtay, “Fifty years of research in scheduling–theory and applications,” Eur J Oper Res, 2025.
[16] A. Bertsimas and J. Tsitsiklis, “Simulated annealing,” Statistical Science, vol. 8, no. 1, 1993, doi: 10.1214/ss/1177011077.
[17] F. Glover, “Tabu Search: A Tutorial,” Interfaces (Providence), vol. 20, no. 4, 1990, doi: 10.1287/inte.20.4.74.
[18] M. Dorigo and C. Blum, “Ant colony optimization theory: A survey,” Theor Comput Sci, vol. 344, no. 2–3, 2005, doi: 10.1016/j.tcs.2005.05.020.
[19] R. Benni, S. R. Umarani, and S. Totad, “A Comprehensive Study of Meta-Heuristic Algorithms for Job Shop Scheduling Optimization,” in 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), IEEE, 2024, pp. 1–10.
[20] C. Blum and A. Roli, “Metaheuristics in Combinatorial Optimization: Overview and Conceptual Comparison,” 2003. doi: 10.1145/937503.937505.
[21] E. Ziegel, “Genetic Algorithms and Engineering Optimization,” Technometrics, vol. 44, no. 1, 2002, doi: 10.1198/tech.2002.s675.
[22] A. E. Smith, “Multi-objective optimization using evolutionary algorithms [Book Review],” IEEE Transactions on Evolutionary Computation, vol. 6, no. 5, pp. 526–526, Apr. 2005, doi: 10.1109/tevc.2002.804322.
[23] M. Andrew, “Introduction to Evolutionary Computing,” 2004. doi: 10.1108/03684920410699216.
[24] Lei, “A genetic algorithm for flexible job shop scheduling with fuzzy processing time,” Int J Prod Res, vol. 48, no. 10, pp. 2995–3013, 2010.
[25] T. C. E. Cheng, J. N. D. Gupta, and G. Wang, “A review of flowshop scheduling research with setup times,” Prod Oper Manag, vol. 9, no. 3, pp. 262–282, 2000.
[26] Rooyani and F. Defersha, “A Two-Stage Multi-Objective Genetic Algorithm for a Flexible Job Shop Scheduling Problem with Lot Streaming,” Algorithms, vol. 15, no. 7, 2022, doi: 10.3390/a15070246.
[27] K. Li, L. Xu, H. Zhang, and J. Chen, “A genetic algorithm for fuzzy identical parallel machine scheduling of minimising total weighted tardiness under resource constraint,” Int J Prod Res, vol. 62, no. 21, 2024, doi: 10.1080/00207543.2024.2323065.
[28] H. Hanif, N. Daud, D. Kurnianingtyas, I. K. Hutomo, and I. Gunawan, “Cost-effective job sequence optimization for minimizing downtime in flexographic printing with puma optimizer algorithm,” in 2024 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT), IEEE, 2024, pp. 15–20.
[29] Alhijawi and A. Awajan, “Genetic algorithms: theory, genetic operators, solutions, and applications,” 2024. doi: 10.1007/s12065-023-00822-6.
[30] P. K. Ariningsih, T. Iswari, K. D. Poetra, and Y. M. K. Aritonang, “Sequential Routing-Loading Algorithm for Optimizing One-Door Container Closed-Loop Logistics Operations,” Jurnal Optimasi Sistem Industri, vol. 19, no. 2, 2020, doi: 10.25077/josi.v19.n2.p122-132.2020.
[31] N. Zhafira, F. Afrinaldi, and Taufik, “An Application of Genetic Algorithm in Determining Salesmen’s Routes: A Case Study,” Jurnal Optimasi Sistem Industri, vol. 17, no. 1, 2018, doi: 10.25077/josi.v17.n1.p26-34.2018.
[32] M. Utama, L. R. Ardiansyah, and A. K. Garside, “Penjadwalan Flow shop Untuk Meminimasi Total Tardiness Menggunakan Algoritma Cross Entropy–Algoritma Genetika,” Jurnal Optimasi Sistem Industri, vol. 18, no. 2, 2019, doi: 10.25077/josi.v18.n2.p133-141.2019.
[33] S. S. Tjandra, F. Setiawan, and H. Salsabila, “Application of Genetic Algorithms to Solve MTSP Problems with Priority (Case Study at the Jakarta Street Lighting Service),” Jurnal Optimasi Sistem Industri, vol. 21, no. 2, 2022, doi: 10.25077/josi.v21.n2.p75-86.2022.
[34] A. A. Said, Dasar Desain Dwimatra, vol. 53, no. 9. 2012.
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