Genetic algorithms for the scheduling in additive manufacturing

  1. Salvador Castillo Rivera 1
  2. Juan de Antón Heredero 1
  3. Ricardo del Olmo Martínez 2
  4. Javier Pajares Gutiérrez 1
  5. Adolfo López Paredes 1
  1. 1 Universidad de Valladolid

    Universidad de Valladolid

    Valladolid, España


  2. 2 Universidad de Burgos

    Universidad de Burgos

    Burgos, España


International Journal of Production Management and Engineering (IJPME)

ISSN: 2340-4876

Year of publication: 2020

Volume: 8

Issue: 2

Pages: 59-63

Type: Article

DOI: 10.4995/IJPME.2020.12173 DIALNET GOOGLE SCHOLAR lock_openOpen access editor

More publications in: International Journal of Production Management and Engineering (IJPME)


Cited by

  • Scopus Cited by: 1 (31-03-2023)
  • Web of Science Cited by: 1 (14-03-2023)


  • Social Sciences: C

Scopus CiteScore

  • Year 2020
  • CiteScore of the Journal : 0.0
  • Area: Industrial and Manufacturing Engineering Percentile: 1
  • Area: Business and International Management Percentile: 1
  • Area: Strategy and Management Percentile: 0
  • Area: Management Science and Operations Research Percentile: 0

Journal Citation Indicator (JCI)

  • Year 2020
  • Journal Citation Indicator (JCI): 0.28
  • Best Quartile: Q3
  • Area: ENGINEERING, MULTIDISCIPLINARY Quartile: Q3 Rank in area: 100/170


Genetic Algorithms (GAs) are introduced to tackle the packing problem. The scheduling in Additive Manufacturing (AM) is also dealt with to set up a managed market, called “Lonja3D”. This will enable to determine an alternative tool through the combinatorial auctions, wherein the customers will be able to purchase the products at the best prices from the manufacturers. Moreover, the manufacturers will be able to optimize the production capacity and to decrease the operating costs in each case.

Bibliographic References

  • Ahsan, A., Habib, A., Khoda, B. (2015). Resource based process planning for additive manufacturing. Computer-Aided Design, 69, 112-125.
  • Araújo, L., Özcan, E., Atkin, J., Baumers, M., Tuck, C., Hague, R. (2015). Toward better build volume packing in additive manufacturing: classification of existing problems and benchmarks. 26th Annual International Solid Freeform Fabrication Symposium - an Additive Manufacturing Conference, 401-410.
  • Berman, B. (2012). 3-D printing: The new industrial revolution. Business Horizons, 55: 155-162.
  • Canellidis, V., Dedoussis, V., Mantzouratos, N., Sofianopoulou, S. (2006). Preprocessing methodology for optimizing stereolithography apparatus build performance. Computers in Industry, 57, 424-436.
  • Chergui, A., Hadj-Hamoub, K., Vignata, F. (2018). Production scheduling and nesting in additive manufacturing. Computers & Industrial Engineering, 126, 292-301.
  • Demirel, E., Özelkan, E.C., Lim, C. (2018). Aggregate planning with flexibility requirements profile. International Journal of Production Economics, 202, 45-58.
  • Fera, M., Fruggiero, F., Lambiase, A., Macchiaroli, R., Todisco, V. (2018). A modified genetic algorithm for time and cost optimization of an additive manufacturing single-machine scheduling. International Journal of Industrial Engineering Computations, 9, 423-438.
  • Hopper, E., Turton, B. (1997). Application of genetic algorithms to packing problems - A Review. Proceedings of the 2nd Online World Conference on Soft Computing in Engineering Design and Manufacturing, Springer Verlag, London, 279-288.
  • Ikonen, I., Biles, W.E., Kumar, A., Wissel, J.C., Ragade, R.K. (1997). A genetic algorithm for packing three-dimensional non-convex objects having cavities and holes. ICGA, 591-598.
  • Kim, K.H., Egbelu, P.J. (1999). Scheduling in a production environment with multiple process plans per job. International Journal of Production Research, 37, 2725-2753.
  • Lawrynowicz, A. (2011). Genetic algorithms for solving scheduling problems in manufacturing systems. Foundations of Management, 3(2), 7-26.
  • Li, Q., Kucukkoc, I., Zhang, D. (2017). Production planning in additive manufacturing and 3D printing. Computers and Operations Research, 83, 157-172.
  • Milošević, M., Lukić, D., Đurđev, M., Vukman, J., Antić, A. (2016). Genetic Algorithms in Integrated Process Planning and Scheduling–A State of The Art Review. Proceedings in Manufacturing Systems, 11(2), 83-88.
  • Pour, M.A., Zanardini, M., Bacchetti, A., Zanoni, S. (2016). Additive manufacturing impacts on productions and logistics systems. IFAC, 49(12), 1679-1684.
  • Wilhelm, W.E., Shin, H.M. (1985). Effectiveness of Alternate Operations in a Flexible Manufacturing System. International Journal of Production Research, 23(1), 65-79.
  • Xirouchakis, P., Kiritsis, D., Persson, J.G. (1998). A Petri net Technique for Process Planning Cost Estimation. Annals of the CIRP, 47(1), 427-430.
  • Zhang, Y., Bernard, A., Gupta, R.K., Harik, R. (2014). Evaluating the design for additive manufacturing: a process planning perspective. Procedia CIRP, 21, 144-150.