Daniel Porumbel

Daniel Porumbel is an Associate Professor with expertise in operations research and combinatorial optimization. His research focuses on developing algorithms for complex routing problems under uncertainty, particularly capacitated vehicle routing with evidential demands. Key research areas include belief function theory, permutation set-covering problems, and efficient lower bound techniques for routing and location problems.

Research Interests:

• Optimization under uncertainty using evidential demands
• Column generation and local search metaheuristics
• Dual feasible functions for routing optimization