Following the emergence of Elastic Optical Networks (EONs), Machine Learning (ML) has been intensively investigated as a promising methodology to address complex network management tasks, including, e.g., Quality of Transmission (QoT) estimation, fault management, and automatic adjustment of transmission parameters. Though several ML-based solutions for specific tasks have been proposed, how to integrate the outcome of such ML approaches inside Routing and Spectrum Assignment (RSA) models (which address the fundamental planning problem in EONs) is still an open research problem. In this study, we propose a dual-stage iterative RSA optimization framework that incorporates the QoT estimations provided by a ML regressor, used to define lightpaths’ reach constraints, into a Mixed Integer Linear Programming (MILP) formulation. The first stage minimizes the overall spectrum occupation, whereas the second stage maximizes the minimum inter-channel spacing between neighbor channels, without increasing the overall spectrum occupation obtained in the previous stage. During the second stage, additional interference constraints are generated, and these constraints are then added to the MILP at the next iteration round to exclude those lightpaths combinations that would exhibit unacceptable QoT. Our illustrative numerical results on realistic EON instances show that the proposed ML-assisted framework achieves spectrum occupation savings up to 52.4% (around 33% on average) in comparison to a traditional MILP-based RSA framework that uses conservative reach constraints based on margined analytical models.

Machine Learning (ML) is under intense investigation in optical networks as it promises to lead to automation of a variety of management tasks, as amplifier gain equalization, fault recognition, Quality of Transmission (QoT) estimation, and many others. Though several studies focus on each of these specific tasks, the integration of ML-based estimations inside Routing and Spectrum Assignment (RSA) is still largely unexplored.This paper moves towards such integration. We develop a framework that leverages the probabilistic outputs of a ML-based QoT estimator to define the reach constraints in an Integer Linear Programming (ILP) formulation for RSA in an elastic optical network. In this integrated procedure, the RSA problem is solved iteratively by updating the reach constraints based on the outcome of a QoT estimator, to exclude lightpaths with unacceptable QoT. In our numerical evaluation, the proposed integrated method achieves savings in spectrum occupation up to 30% (around 20% on average) compared to traditional ILP-based RSA approaches with reach constraints based on margined analytical models.

We present the Swiss2Grid project, a pilot and demonstration aimed at evaluating the impact of different distributed demand management policies in Smart Grids. The increasing diffusion of decentralised energy generation, especially photovoltaics, can lead to severe imbalances on the electric grid, which could require huge investments in grid infrastructures. The approach proposed by the Swiss2Grid project is to adopt a decentralised approach to load management at the local level. Single households use a local algorithm that, based only on local voltage and frequency measures, shifts the pre-emptible loads in time in order to minimise the costs for the consumer and to maximise the grid stability. In this paper we present the project set-up in Mendrisio, a city in Southern Switzerland, we describe the algorithm principles, and finally we present some preliminary results showing the impact of the Swiss2Grid algorithm on the Low Voltage grid.

Abstract Optimization problems with noisy data solved using stochastic programming or robust optimization approaches require the explicit characterization of an uncertainty set U that models the nature of the noise. Such approaches depend on the modeling of the uncertainty set and suffer from an erroneous estimation of the noise. In this article, we introduce a framework that considers the uncertain data implicitly. We define the concept of Uncertainty Features (UF), which are problem‐specific structural properties of a solution. We show how to formulate an uncertain problem using the Uncertainty Feature Optimization (UFO) framework as a multi‐objective problem. We show that stochastic programming and robust optimization are particular cases of the UFO framework. We present computational results for the Multi‐Dimensional Knapsack Problem (MDKP) and discuss the application of the framework to the airline scheduling problem. © 2011 Wiley Periodicals, Inc. NETWORKS, 2011

Abstract The resource constrained elementary shortest path problem (RCESPP) arises as a pricing subproblem in branch‐and‐price algorithms for vehicle‐routing problems with additional constraints. We address the optimization of the RCESPP and we present and compare three methods. The first method is a well‐known exact dynamic‐programming algorithm improved by new ideas, such as bidirectional search with resource‐based bounding. The second method consists in a branch‐and‐bound algorithm, where lower bounds are computed by dynamic‐programming with state‐space relaxation; we show how bounded bidirectional search can be adapted to state‐space relaxation and we present different branching strategies and their hybridization. The third method, called decremental state‐space relaxation, is a new one; exact dynamic‐programming and state‐space relaxation are two special cases of this new method. The experimental comparison of the three methods is definitely favorable to decrement state‐space relaxation. Computational results are given for different kinds of resources, arising from the capacitated vehicle‐routing problem, the vehicle‐routing problem with distribution and collection, and the vehicle‐routing problem with capacities and time windows. © 2007 Wiley Periodicals, Inc. NETWORKS, 2008