A biased random-key genetic algorithm for routing and wavelength assignment under a sliding scheduled traffic model

The problem of routing and wavelength assignment in optical networks consists in minimizing the number of wavelengths that are needed to route a set of demands, such that demands routed using lightpaths that share common links are assigned to different wavelengths. We present a biased random-key genetic algorithm for approximately solving the problem of routing and wavelength assignment of sliding scheduled lightpath demands in optical networks. In this problem variant, each demand is characterized not only by a source and a destination, but also by a duration and a time window in which it has to be met. Computational experiments show that the numerical results obtained by the proposed heuristic improved upon those obtained by a multistart constructive heuristic. In addition, the biased random-key genetic algorithm obtained much better results than an existing algorithm for the problem, finding solutions that use roughly 50% of the number of wavelengths determined by the latter.