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

  • Qiang Li
  • David Zhang
  • Ibrahim KucukkocEmail author
  • Naihui He
Chapter
  • 44 Downloads
Part of the Nonlinear Systems and Complexity book series (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.

Notes

Acknowledgements

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

References

  1. 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 CrossRefGoogle Scholar
  2. 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 CrossRefGoogle Scholar
  3. 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 CrossRefGoogle Scholar
  4. 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 MathSciNetCrossRefGoogle Scholar
  5. 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 MathSciNetCrossRefGoogle Scholar
  6. 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 MathSciNetCrossRefGoogle Scholar
  7. 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. 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 CrossRefGoogle Scholar
  9. 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 MathSciNetCrossRefGoogle Scholar
  10. 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 CrossRefGoogle Scholar
  11. 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 CrossRefGoogle Scholar
  12. 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. 195–201Google Scholar
  13. 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. 237–247Google Scholar
  14. 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 MathSciNetCrossRefGoogle Scholar
  15. 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 CrossRefGoogle Scholar
  16. 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 CrossRefGoogle Scholar
  17. 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 CrossRefGoogle Scholar
  18. 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 CrossRefGoogle Scholar
  19. 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. 225–235Google Scholar
  20. 20.
    P. Jacobs, 2D Rectangle bin packing in Python (2016). Online material. https://github.com/pellejacobs/2d-rectangle-bin-packing (Accessed: 19.12.2019)

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Qiang Li
    • 1
    • 2
  • David Zhang
    • 1
    • 2
  • Ibrahim Kucukkoc
    • 3
    Email author
  • Naihui He
    • 1
  1. 1.College of Engineering, Mathematics and Physical SciencesUniversity of ExeterExeterUK
  2. 2.College of Mechanical EngineeringChongqing UniversityChongqingChina
  3. 3.Balikesir UniversityIndustrial Engineering DepartmentBalikesirTurkey

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