The inola Advanced Optimization Core is a reliable, self-learning, heuristic optimization technology which is particularly useful for solving practical complex constrained operations research problems. Generic heuristic optimization methods have the disadvantage of not being able to provide evidence for global optimal solutions, so the computed solution should be delivered fast and of high quality.In the presentation, the importance of efficient algorithm implementation will be demonstrated by means of practical routing tasks, which has been implemented with the inola AOC. Using different algorithm designs, the impact on performance will be analyzed by pre-post comparison. Small changes in just parts of selected algorithms can lead to a significant improvement in the application’s performance. In addition to the importance of efficient algorithm implementation another aspect, why heuristic optimization methods can fail, is the lack of a proper solution space exploration. The optimization method shall only cut-off areas of the solution space, in which the optimum will never be found, which is by default a challenge of optimization.