A drivers' performance assessment model based on fuel economy measurements

Jenny Díaz Ramírez, José Ignacio Huertas

Resultado de la investigación

Resumen

Currently, it is a common practice within transport companies to reduce fuel consumption of their fleet by awarding best drivers with public symbolic recognitions, as part of their programs to promote efficient driving. Usually fuel economy (FE) is the key performance indicator used to evaluate them. However, FE depends on several other parameters such as the working route, vehicle weight and vehicle technology. Therefore, companies with a diverse fleet composition require a fair KPI to select their best drivers. In this work, we present a model to assess drivers' performance based on FE measurements. Based on multivariate statistical analysis of one-year FE data of an urban and interurban bus transit company, we found that drivers' FE exhibit a normal distribution when they are grouped within three categories: (a) the route, representing the driving cycle; (b) the vehicle age, representing the engine technology, and (c) the number of axles, representing the weight of the vehicle. Thus, the standard statistical analysis to identify outliers was used to identify best drivers and vehicles that require maintenance.

Idioma originalEnglish
Título de la publicación alojadaProceedings of the International Conference on Industrial Engineering and Operations Management
EditorialIEOM Society
Páginas457-458
Número de páginas2
Volumen2019
EdiciónMAR
EstadoPublished - mar 2019
Evento9th International Conference on Industrial Engineering and Operations Management, IEOM 2019 - Bangkok
Duración: 5 mar 20197 mar 2019

Serie de la publicación

NombreProceedings of the International Conference on Industrial Engineering and Operations Management

Conference

Conference9th International Conference on Industrial Engineering and Operations Management, IEOM 2019
PaísThailand
CiudadBangkok
Período5/3/197/3/19

All Science Journal Classification (ASJC) codes

  • Strategy and Management
  • Management Science and Operations Research
  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

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