Implementation of supervised statistical data mining algorithm for single machine scheduling
Journal of Advances in Management Research
ISSN: 0972-7981
Article publication date: 26 October 2012
Abstract
Purpose
Machine scheduling plays an important role in most manufacturing industries and has received a great amount of attention from operation researchers. Production scheduling is concerned with the allocation of resources and the sequencing of tasks to produce goods and services. Dispatching rules help in the identification of efficient or optimized scheduling sequences. The purpose of this paper is to consider a data mining‐based approach to discover previously unknown priority dispatching rules for the single machine scheduling problem.
Design/methodology/approach
In this work, the supervised statistical data mining algorithm, namely Bayesian, is implemented for the single machine scheduling problem. Data mining techniques are used to find hidden patterns and rules through large amounts of structured or unstructured data. The constructed training set is analyzed using Bayesian method and an efficient production schedule is proposed for machine scheduling.
Findings
After integration of naive Bayesian classification, the proposed methodology suggests an optimized scheduling sequence.
Originality/value
This paper analyzes the progressive results of a supervised learning algorithm tested with the production data along with a few of the system attributes. The data are collected from the literature and converted into the training data set suitable for implementation. The supervised data mining algorithm has not previously been explored in production scheduling.
Keywords
Citation
Premalatha, S. and Baskar, N. (2012), "Implementation of supervised statistical data mining algorithm for single machine scheduling", Journal of Advances in Management Research, Vol. 9 No. 2, pp. 170-177. https://doi.org/10.1108/09727981211271913
Publisher
:Emerald Group Publishing Limited
Copyright © 2012, Emerald Group Publishing Limited