以資料探勘技術建立宅配業之車輛維修及預警決策支援系統

Abstract

物流宅配業隨著消費行為的改變及電子商務的興起正蓬勃的發展，但是當其業績不斷成長之時，人員薪資、車輛運作與維修費用也隨之攀升；因此，如何有效降低車輛相關的營運成本已是物流宅配業者當前之重要課題。研究由物流配送公司中車輛管理人員的角度出發，發展智慧型的車輛零件預防更換及危險車輛預警系統，透過網路平台協助非專業車輛維修技師的車輛管理人員，能輕易的進行預防性維修（preventive maintenance），即在車輛尚未失效前進行預防更換（preventive replacement）或謹慎地依據生產程序進行保養維修。實作上，研究首先透過資料探勘技術之關聯法則及序列型樣分析，找出不同車型維修零件的相關聯性及先後順序，建立預防性維修知識庫以輔助車輛管理人員進行車輛零件預防更換的決策分析。研究更進一步運用歷史維修記錄透過決策樹技術建立一套危險車輛預測模型並進行模型的驗證，研究結果顯示該危險車輛預測模型之平均正確度（accuracy）可以到達96.98%。研究為使車輛零件維修預防更換及危險車輛預測知識與服務能夠不受時間及空間的限制，將建構Web-Based服務平台並利用.Net網頁建構技術建置此系統，讓車輛管理人員能透過平台以獲得即時最佳的車輛保養、零件更換建議與危險車輛預警服務。最後研究模型將落實在個案公司的車輛管理系統中以驗證方法之正確性與系統之可行性。

As e-commerce has grown exponentially, the business of the distribution service is also growing and expanding quickly in recent years. Nevertheless, from the perspective of the merchant, the maintenance, repair and operations (MRO) fees of vehicles in distribution service is also increasing dramatically. In this project, we develop an intelligent maintenance and replacement system to help the manager and technicians conduct preventive maintenance and replacement for vehicles. Practically, we employ association rule mining and sequential pattern mining methods to analyze the relationships and the priorities among vehicles' components to execute preventive maintenance. Furthermore, we employ the decision tree algorithm to predict the vehicles dangerous based on the historical maintenance lists. In this work, we also validate the performance of the construct model which can achieve 96.98% in accuracy rate. Consequently, it can help the manager and technicians to make decisions on either repairing the vehicles or selling them out. For taking the advantage of Web-based Platform, we adopt the .Net technique to develop the intelligent system based on the proposed methods; therefore, employees can access the services anytime-anywhere via the Internet. Finally, we will realize the system in the distributions service to evaluate the accuracy and feasibility of the proposed model and system in the real operation condition.