The paper investigates efficient allocation of three types of flows (unicast, anycast, multicast) in elastic optical network with dedicated path protection. We model the problem as an integer linear programming and propose efficient solution methods: greedy randomized algorithm (GRA) and column generation (CG)-based approach implemented in two versions, which differ in the method used to find a final problem solution for the selected columns. Then, we evaluate in detail methods efficiency with respect to four reference algorithms. What is more, for CG-based methods we use five different algorithms to find initial sets of columns and, by these means, evaluate how the quality of these methods influences the efficiency of CG-based approaches. The simulation results show that the proposed GRA significantly outperforms reference greedy methods and finds very good solutions (the average gap to optimal result was not higher than 13.4%). The CG-based methods allow to further improve results and obtain solutions very close to optimal ones (for the best CG-based version, the highest average gap to optimal result was 2.1%). Moreover, the results show that the traffic pattern influences the algorithms performance. The methods perform the best for scenarios with high amount of multicast volume while high anycast volume brings the most difficult problem instances. Eventually, the study reveals that the efficiency of CG-based methods depends strongly on the quality of initial columns as well as on the method used to find final solution for the selected columns.

本文研究了具有专用路径保护的弹性光网络中三种类型的流（单播，任播，多播）的有效分配。我们将问题建模为整数线性规划，并提出有效的解决方法：在两个版本中实施的基于贪婪随机算法（GRA）和基于列生成（CG）的方法，这两种方法的区别在于用于为选定的对象找到最终问题的方法列。然后，我们详细评估了相对于四种参考算法的方法效率。而且，对于基于CG的方法，我们使用五种不同的算法来查找初始的列集，并通过这些方法评估这些方法的质量如何影响基于CG的方法的效率。仿真结果表明，提出的GRA明显优于参考贪婪方法，并找到了很好的解决方案（与最佳结果的平均差距不超过13.4％）。基于CG的方法可进一步改善结果，并获得非常接近最佳解决方案的解决方案（对于基于CG的最佳版本，与最佳结果的最高平均差距为2.1％）。此外，结果表明，流量模式会影响算法性能。该方法在具有大量多播量的场景中表现最佳，而任意播量高则带来最困难的问题实例。最终，研究表明基于CG的方法的效率在很大程度上取决于初始色谱柱的质量以及用于为所选色谱柱找到最终解决方案的方法。