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We investigate the Robust Multiperiod Network Design Problem, a generalization of the Capacitated Network Design Problem (CNDP) that, besides establishing flow routing and network capacity installation as in a canonical CNDP, also considers a planning horizon made up of multiple time periods and protection against fluctuations in traffic volumes. As a remedy against traffic volume uncertainty, we propose a Robust Optimization model based on Multiband Robustness (Büsing and D'Andreagiovanni, 2012), a refinement of classical Gamma-Robustness by Bertsimas and Sim (2004) that uses a system of multiple deviation bands. Since the resulting optimization problem may prove very challenging even for instances of moderate size solved by a state-of-the-art optimization solver, we propose a hybrid primal heuristic that combines a randomized fixing strategy inspired by ant colony optimization and an exact large neighbourhood search. Computational experiments on a set of realistic instances from the SNDlib (2010) show that our original heuristic can run fast and produce solutions of extremely high quality associated with low optimality gaps.
We investigate the Robust Multiperiod Network Design Problem, a generalization
of the Capacitated Network Design Problem (CNDP) that, besides establishing flow routing and network capacity installation as in a canonical
CNDP, also considers a planning horizon made up of multiple time periods and protection against fluctuations in traffic volumes. As a remedy against
traffic volume uncertainty, we propose a Robust Optimization model based
on Multiband Robustness (Büsing and D'Andreagiovanni, 2012), a refinement of classical Gamma-Robustness by Bertsimas and Sim (2004) that uses a system of multiple deviation bands. Since the resulting optimization problem
may prove very challenging even for instances of moderate size solved by a state-of-the-art optimization solver, we propose a hybrid primal heuristic that combines a randomized fixing strategy inspired by ant colony optimization and an exact large neighbourhood search. Computational experiments on a set of realistic instances from the SNDlib (2010) show that our original heuristic can run fast and produce solutions of extremely high quality associated with low optimality gaps.
The classical Unit Commitment Problem (UC) consists in determining the production of a set of thermal and hydro power generation units over a given time horizon, in order to meet a forecast energy demand and minimize a cost function. In the last years, in connection with the UC, increasingly attention has been given to active switching, namely the possibility of changing the topology of the transmission network by tripping some of the lines. Active switching has been indeed recognized as an important way to improve capacity and reliability of the network. Modifying the network topology is also very useful in modern electricity systems, where the penetration of on-programmable renewable energy sources is high and tends to be very concentrated in some zones of the network. As a consequence, the interest for automatic and optimized switching procedures is currently spreading.
In this paper, we address the UC when complemented by active switching, thus leading to so-called Unit Commitment with Optimal Transmission Switching (UCOTS). We formulate the UCOTS as a mixed integer linear program, where we unify tight formulations recently proposed in literature for relevant subfamilies of constraints of the problem. Concerning the cost function, in our formulation we approximate the quadratic thermal cost objective function by a piecewise linear function based on perspective cuts and we propose a new simple and effective way to perturb it, with the aim of breaking symmetries induced by optimal transmission switching decision variables. Our original solution approach also exploits specialized branching priorities.
Computational experiments on realistic UCOTS instances highlight that our new framework is able to find optimal and near-optimal solutions in reasonable amount of time by direct use of a state-of-the-art commercial MIP solver.
Revisiting wireless network jamming by SIR-based considerations and Multiband Robust Optimization
(2014)
We revisit the mathematical models for wireless network jamming introduced by Commander et al. (2007,2008): we first point out the strong connections with classical wireless network design and then we propose a new model based on the explicit use of signal-to-interference quantities. Moreover, to address the uncertain nature of the jamming problem and tackle the peculiar right-hand-side uncertainty of the corresponding model, we propose an original robust cutting-plane algorithm drawing inspiration from multiband robust optimization. Finally, we assess the performance of the proposed cutting plane algorithm by experiments on realistic network instances.