1,720,972 research outputs found
Bi-criteria network optimization: problems and algorithms
Full text linkSeveral approaches, exact and heuristics, have been designed in order to generate the Pareto frontier for multi-objective combinatorial optimization problems. Although several classes of standard optimization models have been studied in their multi- objective version, there still exists a big gap between the solution techniques and the complexity of the mathematical models that derive from the most recent real world applications. In this thesis such aspect is highlighted with reference to a specific application field, the telecommunication sector, where several emerging optimization problems are characterized by a multi-objective nature. The study of some of these problems, analyzed and solved in the thesis, has been the starting point for an assessment of the state of the art in multicriteria optimization with particular focus on multi-objective integer linear programming. A general two-phase approach for bi-criteria integer network flow problems has been proposed and applied to the bi-objective integer minimum cost flow and the bi-objective minimum spanning tree problem. For both of them the two-phase approach has been designed and tested to generate a complete set of efficient solutions. This procedure, with appropriate changes according to the specific problem, could be applied on other bi-objective integer network flow problems. In this perspective, this work can be seen as a first attempt in the direction of closing the gap between the complex models associated with the most recent real world applications and the methodologies to deal with multi-objective programming. The thesis is structured in the following way: Chapter 1 reports some preliminary concepts on graph and networks and a short overview of the main network flow problems; in Chapter 2 some emerging optimization problems are described, mathematically formalized and solved, underling their multi-objective nature. Chapter 3 presents the state of the art on multicriteria optimization. Chapter 4 describes the general idea of the solution algorithm proposed in this work for bi-objective integer network flow problems. Chapter 5 is focused on the bi-objective integer minimum cost flow problem and on the adaptation of the procedure proposed in Chapter 4 on such a problem. Analogously, Chapter 6 describes the application of the same approach on the bi-objective minimum spanning tree problem. Summing up, the general scheme appears to adapt very well to both problems and can be easily implemented. For the bi-objective integer minimum cost flow problem, the numerical tests performed on a selection of test instances, taken from the literature, permit to verify that the algorithm finds a complete set of efficient solutions. For the bi-objective minimum spanning tree problem, we solved a numerical example using two alternative methods for the first phase, confirming the practicability of the approach
A New Mixed Integer Linear Programming Formulation For A Maintenance Problem In Italian Railways
No full textThis paper deals with a problem currently receiving increasing at-
tention for its economic implications in the railways companies and in the
safety of travelers. The growing demand for high-speed trains and the compe-
tition with other operators pushes companies to increase rail transport services
to better meet the demand with obvious implications on the company's prot.
However, the high-speed trains have to undergo special maintenance services,
with predetermined constraints ill-structured and dicult to represent math-
ematically. In addition, the maintenance service is highly specialized and can
only run on specic platforms in the stations of destination and is subject to
a more articulated process. This makes models existing in the literature not
adequate to represent these new problems. In particular, formulations of the
problem of routing of trains integrated with the one of the maintenance do not
exist, even if, indeed, the two problems aect each other and formulations of
the two separate parts lead to sub-optimal solutions of the overall problem. In
the talk a new integrated approach is presented by means of a mixed integer
mathematical formulation and computational results discussed.This paper deals with a problem currently receiving increasing at-
tention for its economic implications in the railways companies and in the
safety of travelers. The growing demand for high-speed trains and the compe-
tition with other operators pushes companies to increase rail transport services
to better meet the demand with obvious implications on the company's prot.
However, the high-speed trains have to undergo special maintenance services,
with predetermined constraints ill-structured and dicult to represent math-
ematically. In addition, the maintenance service is highly specialized and can
only run on specic platforms in the stations of destination and is subject to
a more articulated process. This makes models existing in the literature not
adequate to represent these new problems. In particular, formulations of the
problem of routing of trains integrated with the one of the maintenance do not
exist, even if, indeed, the two problems aect each other and formulations of
the two separate parts lead to sub-optimal solutions of the overall problem. In
the talk a new integrated approach is presented by means of a mixed integer
mathematical formulation and computational results discussed
Optimal Energy Management of UAV-Based Cellular Networks Powered by Solar Panels and Batteries: Formulation and Solutions
Full text linkWe focus on the problem of managing the energy consumption of a cellular network tailored to cover rural and low-income areas. The considered architecture exploits Unmanned Aerial Vehicles (UAVs) to ensure wireless coverage, as well as Solar Panels (SPs) and batteries installed in a set of ground sites, which provide the energy required to recharge the UAVs. We then target the maximization of the energy stored in the UAVs and in the ground sites, by ensuring the coverage of the territory through the scheduling of the UAV missions over space and time. After providing the problem formulation, we face its complexity, by proposing a decomposition-based approach and by designing a brand-new genetic algorithm. Results, obtained over a set of representative case studies, reveal that there exists a trade-off between the UAVs battery level, the ground sites battery level and the level of coverage. In addition, both the decomposed version and the genetic algorithm perform sufficiently close to the integrated model, with a strong improvement in the computation times
Multi-objective mathematical programming for optimally sizing and managing battery energy storage for solar photovoltaic system integration of a multi-apartment building
No full textThis article presents a novel mathematical formulation to solve the problem of optimally sizing and managing battery energy storage for the solar photovoltaic system integration of a multi-apartment building. The aim is the maximization of the collective self-consumption maintaining control over time of the energy sold and bought and the monitoring of the state of batteries while ranging from minimum to maximum levels. This is obtained through a new mathematical programming model with three different objective functions that can be tuned simultaneously to find Pareto optimal solutions to the problem. The computational results in detailed scenarios with real data verify the model's adequacy to deal with the real problem and give a measure of the saving of bought and sold energy
A new approach for train calendar description generation
No full textThis paper describes a new method of generating text starting from a calendar, automatically and in a representation clear to customers. We focus in particular on railway applications. Railway undertakings are trying to improve their communication with commuters, employees and travelers, especially for the frequent occurrences of path modifications implying scattered calendars and not intelligible descriptions appearing in various outputs such as websites, mobile applications, timetable boards, train transport diagrams and books. We propose two alternative approaches for this challenging task. The first one combines a customized set covering problem formulation with a parallel vector generation algorithm. The second one integrates the vector generation problem in the set covering problem into a more complex mathematical model. Our aim is to verify that with a mathematical programming approach it is possible to improve the quality of outcomes in terms of intelligibility. We used a commercial mip solver (CPLEX 12.4) to solve the two models, while we designed a specific parallel algorithm for the vector generation problem in the first approach. The new solutions were tested on several real timetables and compared with the tools used so far
Sleep to stay alive: Optimizing reliability in energy-efficient backbone networks
No full textWe consider the problem of extending device lifetime in backbone networks by exploiting sleep modes. In particular, when the device is put in sleep mode, its lifetime tends to increase. However, the transition between full power and sleep mode tends to decrease the lifetime. We model these two effects in an optimization problem, which considers the management of sleep modes in an operator network. Results, obtained on a realistic case study, show that the average lifetime of the devices in the network can be increased up to 43% compared to an always on solution
Optimal sustainable management of backbone networks
No full textWe optimally formulate the problem of maximizing the sustainability in a backbone network,
with the goal of balancing network energy savings, device fixing costs, and users utility. Results show that the
proposed solution is able to increase the operator revenue, while both a classical energy-saving approach and
an always on solution tend to increase the operator costs
Optimization models for the installation planning of offshore wind farms
Full text linkChallenges posed by global warming motivate the increasing interest in green energy sources, such as wind energy. To make wind energy attractive from an economic viewpoint, the decision-making problems faced in designing, installing, and maintaining wind farms have to be optimized. In this paper, we focus on the problem of optimally planning the installation process of offshore wind farms, as faced by Vattenfall, a leading European energy company. We first formulate the deterministic version of this Installation Planning of Offshore Wind Farms (IPOWF) problem as a Mixed Integer Linear Programming (MILP) model. From this model, we then derive other MILPs that can provide lower and upper bounds to the optimal installation cost. To cope with weather uncertainty, we also provide a light-robust MILP. These models are tested on real-life instances corresponding to two wind farms that Vattenfall has recently built in Denmark and Germany. Computational results show that tight primal and dual solutions can be computed for the deterministic case with the proposed models and that the light-robust model yields solutions that are robust to weather uncertainty
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