Managing the air cargo offload problems using rough set theory

This paper focuses on the development of knowledge model for a prediction of air cargo offload. A knowledge model is a model containing a set of knowledge via rules that has been obtained from mining certain amount of data. These rules might help the management in major decision making such as setting up new strategy. In this study, an intelligent technique for data mining called a rough set theory was used for the knowledge modelling. Rough set technique has been used based on its capability of handling uncertain data often occurs in the real world problem. As a result, a rough classifier was produced and has been used for offload prediction in four travelling sectors. A total of 267 data were obtained from a Malaysian air cargo company. There were eight attributes used as input and one attribute as an output. Data has been through a pre-processing stage to facilitate the requirement of the modelling process. A total of ten experiments using ten sets of different data have been conducted. The best model was selected from the total models generated from the experiment. The model has given a promising result with 100% accuracy. The rules obtained have contributing to offload problem knowledge but need further investigation for more comprehensive knowledge decision model.