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Batch Delivery Considerations in Additive Manufacturing Machine Scheduling Problem

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Operations Research and Data Science in Public Services (AIROYoung 2022)

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Abstract

As a disruptive technology, additive manufacturing (AM) is attracting both researchers and practitioners thanks to its manufacturing ability. Several AM technologies have been developed based on the needs of the industry to fabricate parts through different materials, such as polymers, ceramics and metals. Having a powder-bed fusion AM process, Selective Laser Melting (SLM) enables the direct production of highly customized complex geometries in high-quality lightweight metals. This study focuses on the scheduling of multiple SLM machines to produce part orders received from geographically dispersed customers. Each order may contain more than one part, which may be processed in any batch on any suitable machine. However, the delivery of an order requires that all individual part orders received from the same customer have been completed, to minimize delivery costs. Although there is limited research on multiple AM machine scheduling problems in the literature, none of them considered batch delivery. Following the detailed description of the problem, a genetic algorithm (GA) approach has been proposed to solve a numerical example and the results are presented.

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References

  1. Li, Z., et al.: The influence of scan length on fabricating thin-walled components in selective laser melting. Int. J. Mach. Tools Manuf 126, 1–12 (2018)

    Article  Google Scholar 

  2. Li, Q., Kucukkoc, I., Zhang, D.Z.: Production planning in additive manufacturing and 3D printing. Comput. Oper. Res. 83, 157–172 (2017)

    Article  MathSciNet  Google Scholar 

  3. Kucukkoc, I.: MILP models to minimise makespan in additive manufacturing machine scheduling problems. Comput. Oper. Res. 105, 58–67 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  4. Chergui, A., Hadj-Hamou, K., Vignat, F.: Production scheduling and nesting in additive manufacturing. Comput. Ind. Eng. 126, 292–301 (2018)

    Article  Google Scholar 

  5. Ryan, K.R., et al.: Additive manufacturing (3D printing) of electrically conductive polymers and polymer nanocomposites and their applications. eScience 2(4): 365–381 (2022)

    Google Scholar 

  6. Li, Q., et al.: A dynamic order acceptance and scheduling approach for additive manufacturing on-demand production. Int. J. Adv. Manuf. Technol. 105(9), 3711–3729 (2019)

    Article  Google Scholar 

  7. Qian, Q., et al.: 3D reactive inkjet printing of bisphenol A-polycarbonate. Addit. Manuf. 54, 102745 (2022)

    Google Scholar 

  8. Derby, B.: Additive manufacture of ceramics components by inkjet printing. Engineering 1(1), 113–123 (2015)

    Article  Google Scholar 

  9. Kachit, M., et al.: Direct-ink writing and compression behavior by in situ micro-tomography of architectured 316L scaffolds with a two-scale porosity. J. Market. Res. 20, 1341–1351 (2022)

    Google Scholar 

  10. Melchels, F.P.W., Feijen, J., Grijpma, D.W.: A review on stereolithography and its applications in biomedical engineering. Biomaterials 31(24), 6121–6130 (2010)

    Article  Google Scholar 

  11. Huh, J.T., et al.: Chapter 74 - Three-dimensional bioprinting for tissue engineering. In: Lanza, et al., R., (eds.), Principles of Tissue Engineering, 5th edn., pp. 1391–1415 Academic

    Google Scholar 

  12. Liu, F., et al.: Optimising the process parameters of selective laser melting for the fabrication of Ti6Al4V alloy. Rapid Prototyping J. 24(1), 150–159 (2018)

    Article  Google Scholar 

  13. Kucukkoc, I., Li, Q., Zhang, D.Z.: Increasing the utilisation of additive manufacturing and 3D printing machines considering order delivery times. In: 19th International Working Seminar on Production Economics. Innsbruck, Austria (2016)

    Google Scholar 

  14. Fera, M., et al.: A modified genetic algorithm for time and cost optimization of an additive manufacturing single-machine scheduling. Int. J. Ind. Eng. Comput. 9(4), 423–438 (2018)

    Google Scholar 

  15. Dvorak, F., Micali, M., Mathieu, M.: Planning and scheduling in additive manufacturing. Intel. Artif. 21, 40–52 (2018)

    Article  Google Scholar 

  16. Kucukkoc, I., et al.: Scheduling of multiple additive manufacturing and 3D printing machines to minimise maximum lateness. In: 20th International Working Seminar on Production Economics. Innsbruck, Austria (2018)

    Google Scholar 

  17. Kucukkoc, I., Li, Z., Li, Q.: 2D nesting and scheduling in metal additive manufacturing. In: Communications in Computer and Information Science, pp. 97–112 (2021)

    Google Scholar 

  18. Kucukkoc, I.: Metal additive manufacturing: nesting versus scheduling. In: Optimization and Data Science: Trends and Applications. Springer International Publishing, Cham (2021)

    Google Scholar 

  19. Che, Y., et al.: Machine scheduling with orientation selection and two-dimensional packing for additive manufacturing. Comput. Oper. Res. 130, 105245 (2021)

    Article  MathSciNet  MATH  Google Scholar 

  20. Aloui, A., Hadj-Hamou, K.: A heuristic approach for a scheduling problem in additive manufacturing under technological constraints. Comput. Ind. Eng. 154, 107115 (2021)

    Article  Google Scholar 

  21. Alicastro, M., et al.: A reinforcement learning iterated local search for makespan minimization in additive manufacturing machine scheduling problems. Comput. Oper. Res. 131 (2021)

    Google Scholar 

  22. Rohaninejad, M., et al.: A hybrid learning-based meta-heuristic algorithm for scheduling of an additive manufacturing system consisting of parallel SLM machines. Int. J. Product. Res. 1–21 (2021)

    Google Scholar 

  23. Kapadia, M.S., et al.: A genetic algorithm for order acceptance and scheduling in additive manufacturing. Int. J. Product. Res. 1–18 (2021)

    Google Scholar 

  24. Castillo-Rivera, S.: Maximum utilization in operations scheduling for multiple machines and batches in additive manufacturing. Digit. Manuf. Technol. 1, 1–14 (2021)

    Google Scholar 

  25. Arık, O.A.: Additive manufacturing scheduling problem considering assembly operations of parts. Oper. Res. Int. J. 22, 3063–3087 (2022)

    Article  Google Scholar 

  26. Altekin, F.T., Bukchin, Y.: A multi-objective optimization approach for exploring the cost and makespan trade-off in additive manufacturing. Eur. J. Oper. Res. (2021)

    Google Scholar 

  27. Hu, K., Che, Y., Zhang, Z.: Scheduling unrelated additive manufacturing machines with practical constraints. Comput. Oper. Res. 144, 105847 (2022)

    Article  MathSciNet  MATH  Google Scholar 

  28. Zehetner, D., Gansterer, M.: The collaborative batching problem in multi-site additive manufacturing. Int. J. Prod. Econ. 248, 108432 (2022)

    Article  Google Scholar 

  29. Kim, J., Kim, H.-J.: An exact algorithm for an identical parallel additive machine scheduling problem with multiple processing alternatives. Int. J. Prod. Res. 60(13), 4070–4089 (2022)

    Article  Google Scholar 

  30. Kim, Y.J., Kim, B.S.: Part-grouping and build-scheduling with sequence-dependent setup time to minimize the makespan for non-identical parallel additive manufacturing machines. Int. J. Adv. Manufact. Technol. 119(3), 2247–2258 (2022)

    Article  Google Scholar 

  31. Wu, Q., et al.: Online order scheduling of multi 3D printing tasks based on the additive manufacturing cloud platform. J. Manuf. Syst. 63, 23–34 (2022)

    Article  Google Scholar 

  32. Oh, Y., et al.: Nesting and scheduling problems for additive manufacturing: a taxonomy and review. Addit. Manuf. 36, 101492 (2020)

    Google Scholar 

  33. Brucker, P., et al.: Scheduling a batching machine. J. Sched. 1(1), 31–54 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  34. Cheng, T.C.E., Chen, Z.L., Oguz, C.: One-machine batching and sequencing of multiple-type items. Comput. Oper. Res. 21(7), 717–721 (1994)

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgements

The author acknowledges the financial support received from Balikesir University Scientific Research Projects department under grant number BAP-2022-086.

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Correspondence to Ibrahim Kucukkoc .

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Kucukkoc, I. (2023). Batch Delivery Considerations in Additive Manufacturing Machine Scheduling Problem. In: Cosmi, M., Peirano, L., Raffaele, A., Samà, M. (eds) Operations Research and Data Science in Public Services. AIROYoung 2022. AIRO Springer Series, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-031-34546-3_4

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