@article{10.11648/j.ijefm.20221004.12,
  author = {Manoj Kumar Jha and Hellon G. Ogallo},
  title = {Using Machine Learning for the Development of a Maintenance Management System: Case Study of Kenya},
  journal = {International Journal of Economics, Finance and Management Sciences},
  volume = {10},
  number = {4},
  pages = {166-172},
  doi = {10.11648/j.ijefm.20221004.12},
  url = {https://doi.org/10.11648/j.ijefm.20221004.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijefm.20221004.12},
  abstract = {A Maintenance Management System (MMS) was first developed in the 1982 for implementation in the Arizona Department of Transportation in the United States. It allows for a forecast of future maintenance activities for a road network which deteriorates over time. Successive enhancements to the original MMS have been made over the years by different researchers, including some by the first author. The primary enhancements have been in the formulation and solution algorithms. The initial solution algorithms were Linear Programming (LP) and Dynamic Programming (DP), which, in some previous works, were replaced by genetic algorithms due to their efficiency over LP and DP. In this paper, we propose a Machine Learning (ML) framework for the development of a MMS, which can be a better approach than previously developed approaches. The ML framework uses a Python-based solution methodology in conjunction with geo-spatial modeling, which appears more attractive and efficient in working directly with GIS maps and databases. With respect to application, the attention is focused on African countries using Kenya as a case study example. A recent report on state of Kenyan roads found over 35 percent of Kenyan roads to be still in poor condition even though a comparison of the condition of the roads between 2003 and 2018 showed a successive improvement in road condition over the years. Poor road condition affects mobility and, in turn affects the country’s economy. We adopt a Markov Decision Process to predict the maintenance actions to be undertaken for the Kenyan road network in order to keep an acceptable level of service quality over a specified planning horizon. A budget can then be estimated based on the cost of maintenance actions. A case study using Geographic Information System maps and databases demonstrates the effectiveness of the approach. The result shows that an MMS for Kenyan roads can help forecast the maintenance activities to be undertaken over a planning horizon. For more realistic practical applications, using some of our previous works as a guide, an algorithm to decide on the level of deterioration over time can be developed in future works which could consider factors like weather, vehicle mix, and traffic load.},
 year = {2022}
}

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TY  - JOUR
T1  - Using Machine Learning for the Development of a Maintenance Management System: Case Study of Kenya
AU  - Manoj Kumar Jha
AU  - Hellon G. Ogallo
Y1  - 2022/07/12
PY  - 2022
N1  - https://doi.org/10.11648/j.ijefm.20221004.12
DO  - 10.11648/j.ijefm.20221004.12
T2  - International Journal of Economics, Finance and Management Sciences
JF  - International Journal of Economics, Finance and Management Sciences
JO  - International Journal of Economics, Finance and Management Sciences
SP  - 166
EP  - 172
PB  - Science Publishing Group
SN  - 2326-9561
UR  - https://doi.org/10.11648/j.ijefm.20221004.12
AB  - A Maintenance Management System (MMS) was first developed in the 1982 for implementation in the Arizona Department of Transportation in the United States. It allows for a forecast of future maintenance activities for a road network which deteriorates over time. Successive enhancements to the original MMS have been made over the years by different researchers, including some by the first author. The primary enhancements have been in the formulation and solution algorithms. The initial solution algorithms were Linear Programming (LP) and Dynamic Programming (DP), which, in some previous works, were replaced by genetic algorithms due to their efficiency over LP and DP. In this paper, we propose a Machine Learning (ML) framework for the development of a MMS, which can be a better approach than previously developed approaches. The ML framework uses a Python-based solution methodology in conjunction with geo-spatial modeling, which appears more attractive and efficient in working directly with GIS maps and databases. With respect to application, the attention is focused on African countries using Kenya as a case study example. A recent report on state of Kenyan roads found over 35 percent of Kenyan roads to be still in poor condition even though a comparison of the condition of the roads between 2003 and 2018 showed a successive improvement in road condition over the years. Poor road condition affects mobility and, in turn affects the country’s economy. We adopt a Markov Decision Process to predict the maintenance actions to be undertaken for the Kenyan road network in order to keep an acceptable level of service quality over a specified planning horizon. A budget can then be estimated based on the cost of maintenance actions. A case study using Geographic Information System maps and databases demonstrates the effectiveness of the approach. The result shows that an MMS for Kenyan roads can help forecast the maintenance activities to be undertaken over a planning horizon. For more realistic practical applications, using some of our previous works as a guide, an algorithm to decide on the level of deterioration over time can be developed in future works which could consider factors like weather, vehicle mix, and traffic load.
VL  - 10
IS  - 4
ER  - 

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