Navegando por Autor "Fleming, Peter J."
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Item Aggregation Trees for visualization and dimension reduction in many-objective optimization.(2015) Freitas, Alan Robert Resende de; Fleming, Peter J.; Guimarães, Frederico GadelhaThis paper introduces the concept of Aggregation Trees for the visualization of the results of high-dimensional multi-objective optimization problems, or many-objective problems and as a means of performing dimension reduction. The high dimensionality of manyobjective optimization makes it difficult to represent the relationship between objectives and solutions in such problems and most approaches in the literature are based on the representation of solutions in lower dimensions. The method of Aggregation Trees proposed here is based on an iterative aggregation of objectives that are represented in a tree. The location of conflict is also calculated and represented on the tree. Thus, the tree can represent which objectives and groups of objectives are the most harmonic, what sort of conflict is present between groups of objectives, and which aggregations would be helpful in order to reduce the problem dimension.Item Generic Pareto local search metaheuristic for optimization of targeted offers in a bi-objective direct marketing campaign.(2016) Coelho, Vitor Nazário; Oliveira, Thays Aparecida de; Coelho, Igor Machado; Coelho, Bruno Nazário; Fleming, Peter J.; Guimarães, Frederico Gadelha; Ramalhinho, Helena; Souza, Marcone Jamilson Freitas; Talbi, El-Ghazali; Lust, ThibautCross-selling campaigns seek to offer the right products to the set of customers with the goal of maximizing expected profit, while, at the same time, respecting the purchasing constraints set by investors. In this context, a bi-objective version of this NP-Hard problem is approached in this paper, aiming at maximizing both the promotion campaign total profit and the risk-adjusted return, which is estimated with the reward-to-variability ratio known as Sharpe ratio. Given the combinatorial nature of the problem and the large volume of data, heuristic methods are the most common used techniques. A Greedy Randomized Neighborhood Structure is also designed, including the characteristics of a neighborhood exploration strategy together with a Greedy Randomized Constructive technique, which is embedded in a multi-objective local search metaheuristic. The latter combines the power of neighborhood exploration by using a Pareto Local Search with Variable Neighborhood Search. Sets of non-dominated solutions obtained by the proposed method are described and analyzed for a number of problem instances.Item Multi-objective energy storage power dispatching using plug-in vehicles in a smart-microgrid.(2016) Coelho, Vitor Nazário; Coelho, Igor Machado; Coelho, Bruno Nazário; Cohen, Miri Weiss; Reis, Agnaldo José da Rocha; Silva, Sidelmo Magalhães; Souza, Marcone Jamilson Freitas; Fleming, Peter J.; Guimarães, Frederico GadelhaThis paper describes a multi-objective power dispatching problem that uses Plug-in Electric Vehicle (PEV) as storage units.We formulate the energy storage planning as a Mixed-Integer Linear Programming (MILP) problem, respecting PEV requirements, minimizing three different objectives and analyzing three different criteria. Two novel cost-to-variability indicators, based on Sharpe Ratio, are introduced for analyzing the volatility of the energy storage schedules. By adding these additional criteria, energy storage planning is optimized seeking to minimize the following: total Microgrid (MG) costs; PEVs batteries usage; maximum peak load; difference between extreme scenarios and two Sharpe Ratio indices. Different scenarios are considered, which are generated with the use of probabilistic forecasting, since prediction involves inherent uncertainty. Energy storage planning scenarios are scheduled according to information provided by lower and upper bounds extracted from probabilistic forecasts. A MicroGrid (MG) scenario composed of two renewable energy resources, a wind energy turbine and photovoltaic cells, a residential MG user and different PEVs is analyzed. Candidate non-dominated solutions are searched from the pool of feasible solutions obtained during different Branch and Bound optimizations. Pareto fronts are discussed and analyzed for different energy storage scenarios. Perhaps the most important conclusion from this study is that schedules that minimize the total system cost may increase maximum peak load and its volatility over different possible scenarios, therefore may be less robust.