Feasibility study of automatically performing the concrete delivery dispatching through machine learning techniques

Abstract

Purpose – The purpose of this paper is to study the implementation of machine learning (ML) techniques in order to automatically measure the feasibility of performing ready mixed concrete (RMC) dispatching jobs. Design/methodology/approach – Six ML techniques were selected and tested on data that was extracted from a developed simulation model and answered by a human expert. Findings – The results show that the performance of most of selected algorithms were the same and achieved an accuracy of around 80 per cent in terms of accuracy for the examined cases. Practical implications – This approach can be applied in practice to match experts’ decisions. Originality/value – In this paper the feasibility of handling complex concrete delivery problems by ML techniques is studied. Currently, most of the concrete mixing process is done by machines. However, RMC dispatching still relies on human resources to complete many tasks. In this paper the authors are addressing to reconstruct experts’ decisions as only practical solution.