Skip to main content

Heuristic Techniques for Real-Time Order Acceptance and Scheduling in Metal Additive Manufacturing

  • Chapter
  • First Online:
Mathematical Modelling and Optimization of Engineering Problems

Part of the book series: Nonlinear Systems and Complexity ((NSCH,volume 30))

Abstract

In this research, we consider a real-time order acceptance and scheduling (OAS) problem in metal additive manufacturing (MAM) production environment, where the manufacturer with multiple machines makes decisions on the acceptance and scheduling of dynamic arriving part orders simultaneously. The objective is to maximize profit per unit time within the planning horizon. An MAM machine is a kind of batch processing machine (BPM) in which a batch of non-identical parts can be processed simultaneously as a production job according to its capacity, and the process time of the job is a function of the properties of all parts assigned to this job as well as the specifications of the MAM machine to conduct this job. This is the first time that a real-time OAS problem is considered in MAM production environment with capacity and due date constraints. We define the problem and propose a mathematical formulation. As this problem is shown to be strongly NP-hard, meta-heuristic procedures based on various selection rules are proposed for the generation of feasible schedule results. The difference of bad schedule results from those good ones is investigated first according to the results obtained with the stochastic selection. Afterwards, the performance of non-random selection rules is evaluated by comparing with the best and the worst results from the stochastic selection. Experimental tests indicate that the proposed non-random selection rules are able to provide promising schedule results without iteration.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. R. Jiang, R. Kleer, F.T. Piller, Predicting the future of additive manufacturing: a Delphi study on economic and societal implications of 3D printing for 2030. Technol. Forecast. Soc. Change 117, 84–97 (2017). https://doi.org/10.1016/j.techfore.2017.01.006

    Article  Google Scholar 

  2. S.A.M. Tofail, E.P. Koumoulos, A. Bandyopadhyay, S. Bose, L. O-Donoghue, C. Charitidis, Additive manufacturing: scientific and technological challenges, market uptake and opportunities. Mater. Today 21, 22–37 (2017). https://doi.org/10.1016/j.mattod.2017.07.001

    Article  Google Scholar 

  3. L.E. Murr, S.M. Gaytan, D.A. Ramirez, E. Martinez, J. Hernandez, K.N. Amato, P.W. Shindo, F.R. Medina, R.B. Wicker, Metal fabrication by additive manufacturing using laser and electron beam melting technologies. J. Mater. Sci. Technol. 28, 1–14 (2012). https://doi.org/10.1016/S1005-0302(12)60016-4

    Article  Google Scholar 

  4. Q. Li, I. Kucukkoc, D.Z. Zhang, Production planning in additive manufacturing and 3D printing. Comput. Oper. Res. 83, 1339–1351 (2017). https://doi.org/10.1016/j.cor.2017.01.013

    Article  MathSciNet  Google Scholar 

  5. S.A. Slotnick, Order acceptance and scheduling: a taxonomy and review. Eur. J. Oper. Res. 212, 1–11 (2011). https://doi.org/10.1016/j.ejor.2010.09.042

    Article  MathSciNet  Google Scholar 

  6. H.F. Rahman, R. Sarker, D. Essam, A real-time order acceptance and scheduling approach for permutation flow shop problems. Eur. J. Oper. Res. 247, 488–503 (2015). https://doi.org/10.1016/j.ejor.2015.06.018

    Article  MathSciNet  Google Scholar 

  7. M. Khalili, M. Esmailpour, B. Naderi, The production-distribution problem with order acceptance and package delivery: models and algorithm. Manuf. Rev. 3, 18 (2016). https://doi.org/10.1051/mfreview/2016018

    Google Scholar 

  8. A. Noroozi, M.M. Mazdeh, M. Heydari, M. Rasti-Barzoki, Coordinating order acceptance and integrated production-distribution scheduling with batch delivery considering Third Party Logistics distribution. J. Manuf. Syst. 46, 29–45 (2018). https://doi.org/10.1016/j.jmsy.2017.11.001

    Article  Google Scholar 

  9. T. Aouam, K. Geryl, K. Kumar, N. Brahimi, Production planning with order acceptance and demand uncertainty. Comput. Oper. Res. 91, 145–159 (2018). https://doi.org/10.1016/j.cor.2017.11.013

    Article  MathSciNet  Google Scholar 

  10. F. Calignano, D. Manfredi, E. Ambrosio, S. Biamino, M. Lombbardi, E. Atzeni, A. Salmi, P. Minetola, L. Iuliano, P. Fino, Overview on additive manufacturing technologies. Proc. IEEE 105, 593–612 (2017). https://doi.org/10.1109/JPROC.2016.2625098

    Article  Google Scholar 

  11. M. Khorram Niaki, F. Nonino, Additive manufacturing management: a review and future research agenda. Int. J. Prod. Res. 55, 1419–1439 (2017). https://doi.org/10.1080/00207543.2016.1229064

    Article  Google Scholar 

  12. I. Kucukkoc, Q. Li, D.Z. Zhang, Increasing the utilisation of additive manufacturing and 3D printing machines considering order delivery times, in Nineteenth International Working Seminar on Production Economics, Innsbruck, Austria, vol. 3 (2016), pp. 195–201

    Google Scholar 

  13. I. Kucukkoc, Q. Li, N. He, D. Zhang, Scheduling of multiple additive manufacturing and 3D printing machines to minimise maximum lateness, in: Twentieth International Working Seminar on Production Economics, Innsbruck, Austria, vol. 1 (2018), pp. 237–247

    Google Scholar 

  14. I. Kucukkoc, MILP models to minimise makespan in additive manufacturing machine scheduling problems. Comput. Oper. Res. 105, 58–67 (2019). https://doi.org/10.1016/j.cor.2019.01.006

    Article  MathSciNet  Google Scholar 

  15. X. Li, K. Zhang, Single batch processing machine scheduling with two-dimensional bin packing constraints. Int. J. Prod. Econ. 196, 113–121 (2018). https://doi.org/10.1016/j.ijpe.2017.11.015

    Article  Google Scholar 

  16. J.P. Rudolph, C. Emmelmann, A cloud-based platform for automated order processing in additive manufacturing. Procedia CIRP 63, 412–417 (2017). https://doi.org/10.1016/j.procir.2017.03.087

    Article  Google Scholar 

  17. K. Ransikarbum, S. Ha, J. Ma, N. Kim, Multi-objective optimization analysis for part-to-Printer assignment in a network of 3D fused deposition modeling. J. Manuf. Syst. 43, 35–46 (2017). https://doi.org/10.1016/j.jmsy.2017.02.012

    Article  Google Scholar 

  18. L. Zhou, L. Zhang, Y. Laili, C. Zhao, Y. Xiao, Multi-task scheduling of distributed 3D printing services in cloud manufacturing. Int. J. Adv. Manuf. Technol. (2018). https://doi.org/10.1007/s00170-017-1543-z

    Book  Google Scholar 

  19. Q. Li, I. Kucukkoc, N. He, D. Zhang, S. Wang, Order acceptance and scheduling in metal additive manufacturing: an optimal foraging approach, in Twentieth International Working Seminar on Production Enconomics, Innsbruck, Austria, vol. 1 (2018), pp. 225–235

    Google Scholar 

  20. P. Jacobs, 2D Rectangle bin packing in Python (2016). Online material. https://github.com/pellejacobs/2d-rectangle-bin-packing (Accessed: 19.12.2019)

Download references

Acknowledgements

The third author (I.K.) acknowledges the financial support received from Balikesir University—Scientific Research Projects Department under grant number BAP-2018-131.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ibrahim Kucukkoc .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Li, Q., Zhang, D., Kucukkoc, I., He, N. (2020). Heuristic Techniques for Real-Time Order Acceptance and Scheduling in Metal Additive Manufacturing. In: Machado, J., Özdemir, N., Baleanu, D. (eds) Mathematical Modelling and Optimization of Engineering Problems. Nonlinear Systems and Complexity, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-37062-6_1

Download citation

Publish with us

Policies and ethics