In the noisy intermediate-scale quantum era, quantum annealing (QA) has shown great potential for solving large-scale combinatorial optimization problems. However, challenges remain due to limited hardware capacity and the difficulty of selecting appropriate penalty parameters that enforce constraints without degrading solution quality.

This dissertation develops scalable QA techniques, penalty parameter optimization, annealing schedule design, and hybrid quantum annealing, and applies them to port logistics and general combinatorial optimization problems.