Hostname: page-component-78c5997874-m6dg7 Total loading time: 0 Render date: 2024-11-11T03:54:38.332Z Has data issue: false hasContentIssue false
  • English
  • Français

A review of machine learning in dynamic scheduling of flexible manufacturing systems

Published online by Cambridge University Press:  27 July 2001

PAOLO PRIORE ,
DAVID DE LA FUENTE ,
ALBERTO GOMEZ  and
JAVIER PUENTE
PAOLO PRIORE
Affiliation:
ETSII e II, Campus de Viesques, 33204 Gijón, Spain
DAVID DE LA FUENTE
Affiliation:
ETSII e II, Campus de Viesques, 33204 Gijón, Spain
ALBERTO GOMEZ
Affiliation:
ETSII e II, Campus de Viesques, 33204 Gijón, Spain
JAVIER PUENTE
Affiliation:
ETSII e II, Campus de Viesques, 33204 Gijón, Spain
Get access Rights & Permissions [Opens in a new window]

Abstract

A common way of dynamically scheduling jobs in a flexible manufacturing system (FMS) is by means of dispatching rules. The problem of this method is that the performance of these rules depends on the state the system is in at each moment, and no single rule exists that is better than the rest in all the possible states that the system may be in. It would therefore be interesting to use the most appropriate dispatching rule at each moment. To achieve this goal, a scheduling approach which uses machine learning can be used. Analyzing the previous performance of the system (training examples) by means of this technique, knowledge is obtained that can be used to decide which is the most appropriate dispatching rule at each moment in time. In this paper, a review of the main machine learning-based scheduling approaches described in the literature is presented.

Keywords

Discrete SimulationDispatching RulesDynamic SchedulingFlexible Manufacturing SystemsMachine Learning
Type
Research Article
Information
AI EDAM , Volume 15 , Issue 3 , June 2001 , pp. 251 - 263
Copyright
2001 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)