Regression rationing of labour costs based on the estimation of their actual values by neural network modelling

Abstract

Introduction. Labour rationing is an integral part of effective management of construction production. It is proved by the experience of economically developed countries, where labour rationing is connected with all spheres of enterprises: industrial, technical, organizational, financial, economic and social. Modern methods of labour rationing were created by specialists from economically developed countries. The purpose of this article is to improve the efficiency of the construction industry in the Republic of Iraq by adapting modern labour cost standards to the construction industry. Materials and methods. The method of neural network modelling was used in the work. Results. The networks under consideration were tested to obtain labour costs based on the implementation of production standards, which are known to be the inverse of labour costs. As a result of the experiment, instead of actual labour costs the actual output was introduced, and the inverse value was calculated using the output standards obtained from the neural network modelling. Conclusions. The presented excursus on the labour rationing methods used makes it clear that the creation of appropriate databases requires significant costs and time. Therefore, another alternative to this approach is to use already developed regulatory databases that can be adapted to the construction industry in the Republic of Iraq. In order to implement such an approach, it is necessary to analyze the existing databases and establish such an up-to-date database that would have the greatest correspondence with the actual labour costs specific to the construction industry of the Republic of Iraq. As a generalized conclusion about the practical result of the presented development, a stepwise regression methodology for the formation of labour costs for a selected type of work is presented.