Using Data Mining Techniques in Providing Knowledge for Decision Support System (Case Study: Tehran Bus Transportation System)

Document Type : Scientific - Research

Authors

1 MSc. Grad., Department of Industrial Engineering, Islamic Azad University, South Tehran Branch, Tehran, Iran

2 Professor, School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

Abstract

Using Data Mining techniques, the committed violations by Tehran Bus Company have been analyzed and consequently the hidden knowledge among data has been discovered in this research. First, the data from committed violations by Tehran Bus Company within 2012 to 2015 have been gathered and made ready for the program. The next stage is to extract knowledge through the algorithms of Association Rules and Decision Tree. Identifying the companies with poor performance and high rate of violations and considering the obvious correlation between them in different seasons are among the noteworthy results which are the outputs of the above-mentioned algorithms. The chief managers of organizations should use the Decision Support Systems (DSS) to make decisions in case of uncertainty. The results of the research help managers make decisions about achieving the ultimate organizational goal which is the satisfaction of people. Finding the root of the violations and determining the frequent seasons and months in which the violations are committed can be the cause of instructions and procedures which obligate contractors to precise implementation of regulations, decrease of committed violations and on the whole, improvement in Bus Company. It is a fact, attention to customer satisfaction as a significant and effective factor is essential for achieving stable and sustainable states in all production and industrial units and services firms such as research case study.

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References

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Quarterly Journal of Transportation Engineering
Volume 10, Issue 2 - Serial Number 39
January 2019
Pages 297-310

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History

  • Receive Date: 03 September 2017
  • Revise Date: 26 February 2018
  • Accept Date: 27 February 2018

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