An optimisation approach to road sanitation workforce planning using differential evolution

• Main Authors: Desmond Eseoghene Ighravwe, Sunday Ayoola Oke, Daniel Aikhuele, Abiodun Ojo
• Format: Article
• Language: English
• Published: Elsevier 2020-12-01
• Series: Journal of Urban Management
• Subjects: Road sanitation; Maintenance workforce model; Differential evolution algorithm; Multi-objective
• Online Access: http://www.sciencedirect.com/science/article/pii/S2226585620301564

At present, labour unions of waste disposal agencies and company management are at loggerheads, frequently turning out contradictory sanitation assessments. This reveals a shifting outlook of sanitation accomplishment that should be resolved. Unfortunately, there is scanty research on road sanitation and no study exists on how to determine the important workforce variables of these workers. To solve this research problem, a multi-objective optimisation model is developed and solved using the differential evolution model. The proposed model considered different constraints including workforce size, budgets, and service time. Three conflicting goals of maximization of cleanliness, maximization of workers' effectiveness and minimization of traffic obstruction were incorporated into the model and solved using practical data from a waste disposal agency in a developing country. A key result shows that the system's average workers' turnover rate is 0.2472 while the system's average service failure rate is 0.2518. For each location, the system requires an average of eight workers per period. The worker's average quality of work done is 0.8552. The outcome of the work revealed the feasibility of the model application. It was concluded that the model serves as a basis to evaluate road sanitation workers and may be used for budgetary purposes.