A Weighted Goal Programming Model for Maintenance Workforce Optimisation for a Process Industry

The recent upsurge in economic distress of organisations, and particularly the sustainability challenges faced by them 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. Different analytical models have been developed re-modelled and implemented for maintenance workforce planning in literature but new insights into research focusing on the budgeted funds, workers' distribution and performance metrics (availability and quality of workdone) as well as hiring and firing costs are keenly needed. By responding to this call, the current communication adopts a case-study approach in the optimisation of maintenance workforce variables based on weighted goal programming, genetic algorithm (GA) and Euclidean distance with these parameter treated in a unique manner. A selected optimisation model from literature 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 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 workdone by the workers was above 70%. The results showed that the solution from the weighted goal programming, GA and Euclidean distance were satisfactory.

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