Workforce planning using integer linear programming method: anapplication in mining business

In order to ensure efficient and profitable production in mining operations, human and material resources must be optimized. Therefore, workforce planning is an important strategic issue. The mining sector is a labor-intensive business line based on the use of both labor and machinery. Mining activities cover a number of complex processes, including the extraction, processing and marketing of mines.he mining sector is a labor-intensive business line based on the use of both labor and machinery. Mining activities cover a number of complex processes, including the extraction, processing and marketing of mines. The sector operates depending on many factors such as legal regulations, the vision and mission. This study involves the application of integer linear programming (ILP) method for workforce planning in a mining enterprise. ILP is an optimization method that enables the modeling of complex problems by maximizing or minimizing an objective function under defined constraintshis study involves the application of integer linear programming (ILP) method for workforce planning in a mining enterprise. ILP is an optimization method that enables the modeling of complex problems by maximizing or minimizing an objective function under defined constraints. This method is particularly suitable for the mining sector. In the study, the goal was to perform workforce planning using the ILP model created based on data from a mining company in Gümüşhane. With this planning, the aim was to achieve the highest efficiency by using optimal workforce, and workforce costs were reduced thanks to the determined optimal personnel number. The results obtained indicate that the implementation of ILP increases the operational efficiency of mining enterprises while also reducing labor and resource costs. Additionally, this method contributes to increased employee satisfaction by developing a plan that aligns with employees' personal and professional constraints.