@misc{D'AndreagiovanniKrolikowskiPulaj2013, author = {D'Andreagiovanni, Fabio and Krolikowski, Jonatan and Pulaj, Jonad}, title = {A hybrid primal heuristic for Robust Multiperiod Network Design}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-44081}, year = {2013}, abstract = {We investigate the Robust Multiperiod Network Design Problem, a generalization of the classical Capacitated Network Design Problem that additionally considers multiple design periods and provides solutions protected against traffic uncertainty. Given the intrinsic difficulty of the problem, which proves challenging even for state-of-the art commercial solvers, we propose a hybrid primal heuristic based on the combination of ant colony optimization and an exact large neighborhood search. Computational experiments on a set of realistic instances from the SNDlib show that our heuristic can find solutions of extremely good quality with low optimality gap.}, language = {en} } @inproceedings{D'AndreagiovanniKrolikowskiPulaj2014, author = {D'Andreagiovanni, Fabio and Krolikowski, Jonatan and Pulaj, Jonad}, title = {A hybrid primal heuristic for Robust Multiperiod Network Design}, booktitle = {EvoApplications 2014}, year = {2014}, language = {en} } @misc{D'AndreagiovanniKrolikowskiPulaj2014, author = {D'Andreagiovanni, Fabio and Krolikowski, Jonatan and Pulaj, Jonad}, title = {A Fast Hybrid Primal Heuristic for Multiband Robust Capacitated Network Design with Multiple Time Periods}, issn = {1438-0064}, doi = {10.1016/j.asoc.2014.10.016}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-52862}, year = {2014}, abstract = {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 (B{\"u}sing 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.}, language = {en} } @article{D'AndreagiovanniKrolikowskiPulaj2014, author = {D'Andreagiovanni, Fabio and Krolikowski, Jonatan and Pulaj, Jonad}, title = {A fast hybrid primal heuristic for multiband robust capacitated network design with multiple time periods}, volume = {26}, journal = {Applied Soft Computing}, doi = {10.1016/j.asoc.2014.10.016}, pages = {497 -- 507}, year = {2014}, abstract = {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 (B{\"u}sing 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.}, language = {en} }