Optimizing Candidate Selection for Global Projects Using the K-Median Problem Combined with the Genetic Algorithm (GA)

In today's globalized economy, selecting the most suitable candidates for geographically distributed projects is a complex challenge, requiring a balance between skill alignment, cost efficiency, and geographic constraints. This paper models the candidate selection problem as a k-median problem, where the goal is to minimize the total cost of selecting candidates while meeting project requirements. Due to the NP-hard nature of the problem, we propose using the Genetic Algorithm (GA), a metaheuristic optimization method that encodes candidate locations as "chromosomes" and iteratively improves solutions through selection, crossover, and mutation operations. Experimental results on simulated datasets demonstrate that the GA-based approach effectively identifies near-optimal solutions, significantly reducing selection costs compared to traditional methods. The proposed methodology is scalable and applicable to real-world scenarios such as multinational project management, global workforce allocation, and supply chain optimization. This work provides a robust framework for organizations to optimize candidate selection for global projects while minimizing costs and maximizing performance.