There are many real-world problems that can be formulated as combinatorial optimization problems. They tend to be NP-hard and are hard to solve exactly when the instance sizes are large. As a practical way to solve such problems, heuristic algorithms are often used to find good solutions, though not necessarily optimal, in a reasonable amount of time. Standard basic heuristic strategies include greedy methods and local search algorithms. Such basic strategies are often combined to make more sophisticated methods, called metaheuristics, to find better solutions using more computation time. This talk introduces some of our results on efficient heuristic algorithms based on such strategies.