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

Authors:
Paolo Priore;David De La Fuente;Alberto Gomez;Javier Puente
Affiliations:
ETSII e II, Campus de Viesques, 33204 Gijón, Spain;ETSII e II, Campus de Viesques, 33204 Gijón, Spain;ETSII e II, Campus de Viesques, 33204 Gijón, Spain;ETSII e II, Campus de Viesques, 33204 Gijón, Spain
Venue:
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Year:
2001

Citing 13
Cited 10

Priority rules for job shops with weighted tardiness costs

Management Science
Schedule generation in a flexible manufacturing system: a knowledge-based approach

Decision Support Systems
Integrating scheduling and control functions in computer integrated manufacturing using artificial intelligence

Computers and Industrial Engineering
Evaluation of FMS parameters on overall system performance

Computers and Industrial Engineering
Integration of simulation modeling and inductive learning in an adaptive decision support system

Decision Support Systems - Special issue on model management systems
Supporting complex real-time decision making through machine learning

Decision Support Systems
A study on decision rules of a scheduling model in an FMS

Computers in Industry
Intelligent scheduling

Intelligent scheduling
Implementation of a case-based production scheduling system in C language

ICC&IE-94 Selected papers from the 16th annual conference on Computers and industrial engineering
Learning from examples: a review of machine learning, neural networks and fuzzy logic paradigms

ICC&IE '94 Proceedings of the 17th international conference on Computers and industrial engineering
`` Direct Search'' Solution of Numerical and Statistical Problems

Journal of the ACM (JACM)
Machine Learning Methods for Planning

Machine Learning Methods for Planning
Computers and Intractability: A Guide to the Theory of NP-Completeness

Computers and Intractability: A Guide to the Theory of NP-Completeness

Discovering Dispatching Rules Using Data Mining

Journal of Scheduling
Using mega-fuzzification and data trend estimation in small data set learning for early FMS scheduling knowledge

Computers and Operations Research
A non-parametric learning algorithm for small manufacturing data sets

Expert Systems with Applications: An International Journal
Development of two-level decision tree-based real-time scheduling system under product mix variety environment

Robotics and Computer-Integrated Manufacturing
Using mega-fuzzification and data trend estimation in small data set learning for early FMS scheduling knowledge

Computers and Operations Research
A comparison of machine-learning algorithms for dynamic scheduling of flexible manufacturing systems

Engineering Applications of Artificial Intelligence
Learning IF-THEN priority rules for dynamic job shops using genetic algorithms

Robotics and Computer-Integrated Manufacturing
A genetic algorithm with tabu search procedure for flexible job shop scheduling with transportation constraints and bounded processing times

Computers and Operations Research
Data mining based job dispatching using hybrid simulation-optimization approach for shop scheduling problem

Engineering Applications of Artificial Intelligence
Optimizing execution time predictions of scientific workflow applications in the Grid through evolutionary programming

Future Generation Computer Systems

Quantified Score

Hi-index	0.00

Visualization

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.