Next Article in Journal
The Accurate Method for Computing the Minimum Distance between a Point and an Elliptical Torus
Previous Article in Journal
Acknowledgement to Reviewers of Computers in 2015
Article Menu

Export Article

Open AccessArticle
Computers 2016, 5(1), 3; doi:10.3390/computers5010003

Learning Dispatching Rules for Scheduling: A Synergistic View Comprising Decision Trees, Tabu Search and Simulation

1
Department of Industrial Engineering, Tecnologico de Monterrey, Hermosillo, Sonora Norte 83000, Mexico
2
LUNAM, Université de Nantes, IRCCyN, Institut de Recherche en Communications et Cybernétique de Nantes, UMR CNRS 6597, Nantes, France
*
Author to whom correspondence should be addressed.
Academic Editor: Ata Kaban
Received: 11 December 2015 / Revised: 28 January 2016 / Accepted: 6 February 2016 / Published: 17 February 2016
(This article belongs to the Special Issue Combining Learning and Optimisation)
View Full-Text   |   Download PDF [1535 KB, uploaded 17 February 2016]   |  

Abstract

A promising approach for an effective shop scheduling that synergizes the benefits of the combinatorial optimization, supervised learning and discrete-event simulation is presented. Though dispatching rules are in widely used by shop scheduling practitioners, only ordinary performance rules are known; hence, dynamic generation of dispatching rules is desired to make them more effective in changing shop conditions. Meta-heuristics are able to perform quite well and carry more knowledge of the problem domain, however at the cost of prohibitive computational effort in real-time. The primary purpose of this research lies in an offline extraction of this domain knowledge using decision trees to generate simple if-then rules that subsequently act as dispatching rules for scheduling in an online manner. We used similarity index to identify parametric and structural similarity in problem instances in order to implicitly support the learning algorithm for effective rule generation and quality index for relative ranking of the dispatching decisions. Maximum lateness is used as the scheduling objective in a job shop scheduling environment. View Full-Text
Keywords: shop scheduling; data mining; job shop; dispatching rules; decision trees; Tabu search; simulation shop scheduling; data mining; job shop; dispatching rules; decision trees; Tabu search; simulation
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Shahzad, A.; Mebarki, N. Learning Dispatching Rules for Scheduling: A Synergistic View Comprising Decision Trees, Tabu Search and Simulation. Computers 2016, 5, 3.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Computers EISSN 2073-431X Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top