The document discusses optimization algorithms for planning problems. It begins by introducing Drools Planner, an open source tool for solving planning problems with constraints. It then provides examples of planning problems like employee scheduling and hospital bed planning. These problems involve large search spaces that are intractable to solve via brute force. The document outlines algorithms like construction heuristics, local search, tabu search and simulated annealing that can find high quality solutions in reasonable time by exploring the search space intelligently. It emphasizes the importance of using real-world datasets and constraints to properly evaluate algorithm performance.