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Memorias de investigación
Communications at congresses:
Optimization of an industrial aluminum parts casting process based on simulated annealing
Year:2018
Research Areas
  • Artificial intelligence,
  • Linear programming
Information
Abstract
In this paper, we aim to optimize an aluminum casting process to create parts for the automotive sector. The company has six aluminum injection molding machines to produce different parts. There are a total of 81 injection molds for 160 diff erent types of parts, including molds for a single part, two or even three di erent parts. We must account for constraints regarding which molds can be used in each machine, mold changes (up to four a day, which may be non-simultaneous mold or not coincide with worker shift changes), stock of parts, time set aside for machine breakdowns and scheduled machine maintenance processes. The objectives for a two-week planning period are to maximize accumulated demand satisfaction in the two weeks of the di fferent pieces, minimize the delay in parts production with respect to the specified delivery date, minimize energy costs (electricity and gas consumption) and minimize the total number of mold changes performed. A heuristic is used to derive an initial feasible solution. Simulated annealing is then applied to derive the optimal solution. To do this, di fferent neighborhood definitions are created based on the total or partial elimination or introduction of injections or on injection mold changes, whose use dynamically varies throughout the search process.
International
Si
Congress
29th European Conference on Operational Research
960
Place
Valencia, España
Reviewers
No
ISBN/ISSN
978-84-09-02938-9
Start Date
08/07/2018
End Date
11/07/2018
From page
334
To page
334
Conference handbook
Participants
  • Autor: Antonio Jimenez Martin (UPM)
  • Autor: Alfonso Mateos Caballero (UPM)
  • Autor: Guillermo De Lima Rodríguez
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Grupo de análisis de decisiones y estadística
  • Departamento: Inteligencia Artificial
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