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Implementation of supervised statistical data mining algorithm for single machine scheduling

Implementation of supervised statistical data mining algorithm for single machine scheduling 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Advances in Management Research Emerald Publishing

Implementation of supervised statistical data mining algorithm for single machine scheduling

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Publisher
Emerald Publishing
Copyright
Copyright © 2012 Emerald Group Publishing Limited. All rights reserved.
ISSN
0972-7981
DOI
10.1108/09727981211271913
Publisher site
See Article on Publisher Site

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.

Journal

Journal of Advances in Management ResearchEmerald Publishing

Published: Oct 26, 2012

Keywords: Programming and algorithm theory; Production scheduling; Data mining; Dispatching rule; Learning algorithm; System attributes

References