|
A weighted goal programming modelfor maintenance workforce optimisation ina process industry |
|---|---|
| รหัสดีโอไอ | |
| Creator | Sunday Ayoola Oke |
| Title | A weighted goal programming modelfor maintenance workforce optimisation ina process industry |
| Contributor | Desmond Eseoghene Ighravwe,Kazeem Adekunle Adebiyi |
| Publisher | Research and Technology Transfer Affairs Division.Khon Kaen University. |
| Publication Year | 2560 |
| Journal Title | Asia-Pacific Journal of Science and Technology (APST) |
| Journal Vol. | 22 |
| Journal No. | 4 |
| Page no. | 1-16 |
| Keyword | Meta-heuristics, Euclidean distance, Maintenance workforce planning, Weighted goal programming. |
| URL Website | https://tci-thaijo.org/index.php/APST/index |
| Website title | https://tci-thaijo.org/index.php/APST/article/view/107649 |
| ISSN | 2539-6293 |
| Abstract | The recent upsurge in the economic distress being experiencedbyorganisations, particularly in terms ofthe sustainability challenges they face, raises new concerns that strongly motivate maintenance workforce structural re-modelling. Maintenance workforce planning is an interdisciplinary area,spanning maintenance, industrial engineering, and human-resource planning. There are accounts in the available literature of different analytical models beingdeveloped, re-modelled,and implemented for maintenance workforce planning,but new insights into research focusing on budgeted funds, worker distribution,and performance metrics (availability and quality of work done),as well as hiring and firing costs are badlyneeded. In order to respondto this call, we adopteda case-studyapproach tothe optimisation of maintenance workforce variables based on weighted goal programming, a genetic algorithm (GA),and Euclidean distance with these parameters beingtreated in a unique manner. A selected optimisation model fromthe availableliterature was used to a formulate model for a brewery plant maintenance system. The formulated model was solved using a genetic algorithm (GA), particle swarm optimisation and differential evolution algorithm. The results obtained were compared and it was observed that a GA was the most suitable solution method for the formulated model. The GA results showed that the maximum number of full-time workers hired or fired for the different worker categories were the same (one worker). The values of worker's efficiency and availability were above 80%, while the quality of work doneby the workers was above 70%. The results showed that the solution from the weighted goal programming, GA,and Euclidean distance were satisfactory. |
