Solving a Generalized Network Design Problem Using Hybrid Metaheuristics

Metaheuristics have emerged as a practical and highly effective alternative to traditional exact methods in mixed-integer optimization. Their ability to strike a favorable balance between solution quality and computational time has made them the preferred choice for tackling complex problems and large instances. In this paper, we focus on the Generalized Discrete Cost Multicommodity Network Design Problem (GDCMNDP), a challenging network design problem. We investigate the performance of hybrid metaheuristics, specifically the Genetic Algorithm and the Non-Linear Threshold Algorithm, known for their success in diverse applications. Our proposed collaborative framework, featuring a multistage structure, harnesses the strengths of these metaheuristics. The numerical results obtained demonstrate the effectiveness of our approach in solving various test problems, highlighting its favorable performance.