Prediction of Health-Related Leave Days among Workers in the Energy Sector by Means of Genetic Algorithms

Energies

GregorioFidalgo Valverde¹  AnaSuárez Sánchez¹  Nélida M.Busto Serrano²  FernandoSánchez Lasheras³  AroaGonzález Fuentes⁴ 

[1] Department of Business Administration, School of Mining, Energy and Materials Engineering of Oviedo, University of Oviedo, 33007 Oviedo, Spain;Labor and Social Security Inspectorate. Ministry of Labor and Social Economy, 33007 Oviedo, Spain;Mathematics Department, Faculty of Sciences, University of Oviedo, 33007 Oviedo, Spain;School of Mining, Energy and Materials Engineering of Oviedo, University of Oviedo, 33007 Oviedo, Spain;

关键词: sick leave;    absenteeism;    energy sector;    genetic algorithms (GA);    multivariate adaptive regression splines (MARS);   

DOI  :  10.3390/en13102475

来源: DOAJ

【 摘 要 】

In this research, a model is proposed for predicting the number of days absent from work due to sick or health-related leave among workers in the industry sector, according to ergonomic, social and work-related factors. It employs selected microdata from the Sixth European Working Conditions Survey (EWCS) and combines a genetic algorithm with Multivariate Adaptive Regression Splines (MARS). The most relevant explanatory variables identified by the model can be included in the following categories: ergonomics, psychosocial factors, working conditions and personal data and physiological characteristics. These categories are interrelated, and it is difficult to establish boundaries between them. Any managing program has to act on factors that affect the employees' general health status, process design, workplace environment, ergonomics and psychosocial working context, among others, to achieve success. This has an extensive field of application in the energy sector.

【 授权许可】

Unknown