@inproceedings{D'AndreagiovanniMettPulaj2016, author = {D'Andreagiovanni, Fabio and Mett, Fabian and Pulaj, Jonad}, title = {An (MI)LP-based Primal Heuristic for 3-Architecture Connected Facility Location in Urban Access Network Design}, volume = {9597}, booktitle = {EvoApplications: European Conference on the Applications of Evolutionary Computation. Applications of Evolutionary Computation. 19th European Conference, EvoApplications 2016, Porto, Portugal, March 30 -- April 1, 2016, Proceedings, Part I}, doi = {10.1007/978-3-319-31204-0_19}, pages = {283 -- 298}, year = {2016}, abstract = {We investigate the 3-architecture Connected Facility Location Problem arising in the design of urban telecommunication access networks integrating wired and wireless technologies. We propose an original optimization model for the problem that includes additional variables and constraints to take into account wireless signal coverage represented through signal-to-interference ratios. Since the problem can prove very challenging even for modern state-of-the art optimization solvers, we propose to solve it by an original primal heuristic that combines a probabilistic fixing procedure, guided by peculiar Linear Programming relaxations, with an exact MIP heuristic, based on a very large neighborhood search. Computational experiments on a set of realistic instances show that our heuristic can find solutions associated with much lower optimality gaps than a state-of-the-art solver.}, language = {en} } @article{LeitnerLjubicSinnletal.2016, author = {Leitner, Markus and Ljubic, Ivana and Sinnl, Markus and Werner, Axel}, title = {ILP heuristics and a new exact method for bi-objective 0/1 ILPs: Application to FTTx-network design}, journal = {Computers \& Operations Research}, doi = {10.1016/j.cor.2016.02.006}, year = {2016}, language = {en} } @misc{BetkerGamrathKosiankowskietal.2014, author = {Betker, Andreas and Gamrath, Inken and Kosiankowski, Dirk and Lange, Christoph and Lehmann, Heiko and Pfeuffer, Frank and Simon, Felix and Werner, Axel}, title = {Comprehensive Topology and Traffic Model of a Nation-wide Telecommunication Network}, issn = {1438-0064}, doi = {10.1364/JOCN.6.001038}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-52670}, year = {2014}, abstract = {As a basis for meaningful simulation and optimization efforts with regard to traffic engineering or energy consumption in telecommunication networks, suitable models are indispensable. This concerns not only realistic network topologies, but also models for the geographical distribution and the temporal dynamics of traffic, as well as the assumptions on network components and technology. This paper derives such a model from the practice of a large national carrier. Applying the network and traffic model, we demonstrate its use by presenting various optimization cases related to energy-efficient telecommunication. Here, we focus on load-adaptivity by employing sleep modes to the network hardware, where several constraints on the reconfigurability of the network over time are considered.}, language = {en} } @misc{D'AndreagiovanniFeliciLacalandra2015, author = {D'Andreagiovanni, Fabio and Felici, Giovanni and Lacalandra, Fabrizio}, title = {Revisiting the use of Robust Optimization for optimal energy offering under price uncertainty}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-57128}, year = {2015}, abstract = {We propose a new Robust Optimization method for the energy offering problem of a price-taker generating company that wants to build offering curves for its generation units, in order to maximize its profit while taking into account the uncertainty of market price. Our investigations have been motivated by a critique to another Robust Optimization method, which entails the solution of a sequence of robust optimization problems imposing full protection and defined over a sequence of nested subintervals of market prices: this method presents a number of issues that may severely limit its application and computational efficiency in practice and that may expose a company to the risk of presenting offering curves resulting into suboptimal or even infeasible accepted offers. To tackle all such issues, our method provides for solving one single robust counterpart, considering an intermediate level of protection between null and full protection, and to make energy offers at zero price, practically eliminating the risk of non-acceptance. Computational results on instances provided by our industrial partners show that our new method is able to grant a great improvement in profit.}, language = {en} } @misc{PulajRaymondTheis2015, author = {Pulaj, Jonad and Raymond, Annie and Theis, Dirk}, title = {New Conjectures For Union-Closed Families}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-56804}, year = {2015}, abstract = {The Frankl conjecture, also known as the union-closed sets conjecture, states that there exists an element in at least half of the sets of any (non-empty) union-closed family. From an optimization point of view, one could instead prove that 2a is an upper bound to the number of sets in a union-closed family with n elements where each element is in at most a sets, where a and n are non-negative integers. Formulating these problems as integer programs we observe that computed optimal values do not vary with n. We formalize these observations as conjectures, and show that they are not equivalent to the Frankl conjecture while still having wide-reaching implications if proven true. Finally, we partially prove the new conjectures and discuss possible approaches to solve them completely.}, language = {en} } @misc{D'AndreagiovanniNardin2015, author = {D'Andreagiovanni, Fabio and Nardin, Antonella}, title = {Towards the fast and robust optimal design of Wireless Body Area Networks}, issn = {1438-0064}, doi = {10.1016/j.asoc.2015.04.037}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-55475}, year = {2015}, abstract = {Wireless body area networks are wireless sensor networks whose adoption has recently emerged and spread in important healthcare applications, such as the remote monitoring of health conditions of patients. A major issue associated with the deployment of such networks is represented by energy consumption: in general, the batteries of the sensors cannot be easily replaced and recharged, so containing the usage of energy by a rational design of the network and of the routing is crucial. Another issue is represented by traffic uncertainty: body sensors may produce data at a variable rate that is not exactly known in advance, for example because the generation of data is event-driven. Neglecting traffic uncertainty may lead to wrong design and routing decisions, which may compromise the functionality of the network and have very bad effects on the health of the patients. In order to address these issues, in this work we propose the first robust optimization model for jointly optimizing the topology and the routing in body area networks under traffic uncertainty. Since the problem may result challenging even for a state-of-the-art optimization solver, we propose an original optimization algorithm that exploits suitable linear relaxations to guide a randomized fixing of the variables, supported by an exact large variable neighborhood search. Experiments on realistic instances indicate that our algorithm performs better than a state-of-the-art solver, fast producing solutions associated with improved optimality gaps.}, language = {en} } @article{D'AndreagiovanniNardin2015, author = {D'Andreagiovanni, Fabio and Nardin, Antonella}, title = {Towards the fast and robust optimal design of Wireless Body Area Networks}, volume = {37}, journal = {Applied Soft Computing}, publisher = {Elsevier}, doi = {10.1016/j.asoc.2015.04.037}, pages = {971 -- 982}, year = {2015}, abstract = {Wireless body area networks are wireless sensor networks whose adoption has recently emerged and spread in important healthcare applications, such as the remote monitoring of health conditions of patients. A major issue associated with the deployment of such networks is represented by energy consumption: in general, the batteries of the sensors cannot be easily replaced and recharged, so containing the usage of energy by a rational design of the network and of the routing is crucial. Another issue is represented by traffic uncertainty: body sensors may produce data at a variable rate that is not exactly known in advance, for example because the generation of data is event-driven. Neglecting traffic uncertainty may lead to wrong design and routing decisions, which may compromise the functionality of the network and have very bad effects on the health of the patients. In order to address these issues, in this work we propose the first robust optimization model for jointly optimizing the topology and the routing in body area networks under traffic uncertainty. Since the problem may result challenging even for a state-of-the-art optimization solver, we propose an original optimization algorithm that exploits suitable linear relaxations to guide a randomized fixing of the variables, supported by an exact large variable neighborhood search. Experiments on realistic instances indicate that our algorithm performs better than a state-of-the-art solver, fast producing solutions associated with improved 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} }