A linear programming decision tool for selecting the optimum excavator

Abstract

Previous methods have been developed to predict tracked hydraulic excavator output and associated costs of production, but these fail to provide a “complete” solution to the plant productivity problem. That is, when hiring or purchasing machines plant managers are not normally provided with sufficient detail to optimise the plant selection decision process. The crux of this problem is to choose an appropriate plant item from the vast range available. This paper contributes to resolving this selection process through the application of an optimisation technique, based on linear programming. Specifically, a decision tool for selecting the optimum excavator type for given production scenarios is presented. In achieving this aim, a mass excavation task was specified as the principal decision criterion. Production output and machine hire costs were predicted using both multivariate and bivariate regression models. The decision tool performed well during testing and therefore exhibits significant potential for use by practitioners. The paper concludes with direction for future research work; concentrating on development of a software package for accurately predicting productivity rates and assisting in the plant selection process.