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
    info

    Universidad de Valladolid

    Valladolid, España

    ROR https://ror.org/01fvbaw18

  2. 2 Universidad de Burgos
    info

    Universidad de Burgos

    Burgos, España

    ROR https://ror.org/049da5t36

Journal:
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)

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Metrics

Cited by

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

CIRC

  • 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

Abstract

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

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