Dynamic Weight Configuration of Dispatching Rule Using Machine Learning - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2018

Dynamic Weight Configuration of Dispatching Rule Using Machine Learning

Jong-Ho Shin
  • Function : Author
Chaekyo Lee
  • Function : Author
Sangrae Kim
  • Function : Author
Jun-Gyu Kang
  • Function : Author
  • PersonId : 1049480


The manufacturing execution systems (MES) is one of the key elements consisting smart factory. It is responsible for shop floor control by performing managing resources, dispatching production orders, executing production orders, collecting production data, analyzing production performances, and so on. Through these functionalities, the MES aims high productivity. The dispatching in the MES helps these aims. The selection of job in manufacturing execution systems (MES) is performed by dispatching rule. The dispatching rule is composed of several factors affecting scheduling objective and constraint. In most cases, the dispatching rule is expressed as the weighted sum of factors and the weight moderates the relative importance among factors. To find optimal weight configuration requires heavy calculation burden so that it cannot adapt dynamic order changes. To solve this problem, one of machine learning algorithms is used in this study. The multi-layer perceptron learns the best weight configuration according to orders and predict the best weight configuration for new orders. The proposed method is tested by field data and proved its usefulness.
Fichier principal
Vignette du fichier
472850_1_En_14_Chapter.pdf (697.52 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-02164853 , version 1 (25-06-2019)





Jong-Ho Shin, Chaekyo Lee, Sangrae Kim, Jun-Gyu Kang. Dynamic Weight Configuration of Dispatching Rule Using Machine Learning. IFIP International Conference on Advances in Production Management Systems (APMS), Aug 2018, Seoul, South Korea. pp.110-115, ⟨10.1007/978-3-319-99704-9_14⟩. ⟨hal-02164853⟩
36 View
58 Download



Gmail Facebook X LinkedIn More