1,394 research outputs found
An efficient solver for multi-objective onshore wind farm siting and network integration
Existing planning approaches for onshore wind farm siting and network
integration often do not meet minimum cost solutions or social and
environmental considerations. In this paper, we develop an approach for the
multi-objective optimization of turbine locations and their network connection
using a Quota Steiner tree problem. Applying a novel transformation on a known
directed cut formulation, reduction techniques, and heuristics, we design an
exact solver that makes large problem instances solvable and outperforms
generic MIP solvers. Although our case studies in selected regions of Germany
show large trade-offs between the objective criteria of cost and landscape
impact, small burdens on one criterion can significantly improve the other
criteria. In addition, we demonstrate that contrary to many approaches for
exclusive turbine siting, network integration must be simultaneously optimized
in order to avoid excessive costs or landscape impacts in the course of a wind
farm project. Our novel problem formulation and the developed solver can assist
planners in decision making and help optimize wind farms in large regions in
the future
Locating leak detecting sensors in a water distribution network by solving prize-collecting Steiner arborescence problems
We consider the problem of optimizing a novel acoustic leakage detection system for urban water distribution networks. The system is composed of a number of detectors and transponders to be placed in a choice of hydrants such as to provide a desired coverage under given budget restrictions. The problem is modeled as a particular Prize-Collecting Steiner Arborescence Problem. We present a branch-and-cut-and-bound approach taking advantage of the special structure at hand which performs well when compared to other approaches. Furthermore, using a suitable stopping criterion, we obtain approximations of provably excellent quality (in most cases actually optimal solutions). The test bed includes the real water distribution network from the Lausanne region, as well as carefully randomly generated realistic instance
Networks, Uncertainty, Applications and a Crusade for Optimality
In this thesis we address a collection of Network Design problems which are strongly motivated by applications from Telecommunications, Logistics and Bioinformatics. In most cases we justify the need of taking into account uncertainty in some of the problem parameters, and different Robust optimization models are used to hedge against it. Mixed integer linear programming formulations along with sophisticated algorithmic frameworks are designed, implemented and rigorously assessed for the majority of the studied problems.
The obtained results yield the following observations: (i) relevant real problems can be effectively represented as (discrete) optimization problems within the framework of network design; (ii) uncertainty can be appropriately incorporated into the decision process if a suitable robust optimization model is considered; (iii) optimal, or nearly optimal, solutions can be obtained for large instances if a tailored algorithm, that exploits the structure of the problem, is designed; (iv) a systematic and rigorous experimental analysis allows to understand both, the characteristics of the obtained (robust) solutions and the behavior of the proposed algorithm
SCIP-Jackâa solver for STP and variants with parallelization extensions
This is the author accepted manuscript. The final version is available from Springer Verlag via the DOI in this recordâŻThe Steiner tree problem in graphs is a classical problem that commonly arises in practical applications as one of many variants. While often
a strong relationship between different Steiner tree problem variants can be
observed, solution approaches employed so far have been prevalently problemspecific. In contrast, this paper introduces a general-purpose solver that can
be used to solve both the classical Steiner tree problem and many of its variants without modification. This versatility is achieved by transforming various
problem variants into a general form and solving them by using a state-ofthe-art MIP-framework. The result is a high-performance solver that can be
employed in massively parallel environments and is capable of solving previously unsolved instances.German Federal Ministry of Education and Researc
Reduction techniques for the prize collecting Steiner tree problem and the maximumâweight connected subgraph problem
The concept of reduction has frequently distinguished itself as a pivotal ingredient of exact solving approaches for the Steiner tree problem in graphs. In this article we broaden the focus and consider reduction techniques for three Steiner problem variants that have been extensively discussed in the literature and entail various practical applications: The prizeâcollecting Steiner tree problem, the rooted prizeâcollecting Steiner tree problem and the maximumâweight connected subgraph problem. By introducing and subsequently deploying numerous new reduction methods, we are able to drastically decrease the size of a large number of benchmark instances, already solving more than 90% of them to optimality. Furthermore, we demonstrate the impact of these techniques on exact solving, using the example of the stateâofâtheâart Steiner problem solver SCIPâJack
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