Economical Maintenance and Replacement Decision Making in Fleet Management using Data Mining

Abstract

Recent advances in technologies allowed the development of fleet management systems that enable fleet operators and freight carriers to their fleet performance, so that optimum replacement stage can be calculated or before purchasing new vehicle by analyzing its performance which improve relevant performance as well as cost effective by intervening when such confusion occur in the mind of customer. The aim of this research work is to enhance vehicle life cycle and replacement or disposal of vehicle at the critical point so that cost-effective and good performance both can be achieved by analyzing the performance of vehicle by the use of K-Mean clustering algorithm a decision support system is designed. Analysis of the fleet performance by modeling the process of analyzing through the design and implementation of a system analysis of maintenance & replacement policies in fleet management system using data mining. The latter has three main functionalities: a) it monitors existing vehicles service records, b) it detects maintenance deviations from the normal plan and c) it detects the performance analysis and also its deviation, d) it also detect overall analysis, by the help of major affecting factor