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

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.