Navegando por Autor "Lust, Thibaut"

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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, Thibaut
Cross-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.
Multi-objective approaches for the open-pit mining operational planning problem.
(2012) Coelho, Vitor Nazário; Souza, Marcone Jamilson Freitas; Coelho, Igor Machado; Guimarães, Frederico Gadelha; Lust, Thibaut; Cruz, Raphael Carlos
This work presents three multi-objective heuristic algorithms based on Two-phase Pareto Local Search with VNS (2PPLS-VNS), Multi-objective Variable Neighborhood Search (MOVNS) and Non-dominated Sorting Genetic Algorithm II (NSGA-II). The algorithms were applied to the open-pit-mining operational planning problem with dynamic truck allocation (OPMOP). Approximations to Pareto sets generated by the developed algorithms were compared considering the hypervolume and spacing metrics. Computational experiments have shown the superiority of the algorithms based on VNS methods, which were able to find better sets of non-dominated solutions, more diversified and with an improved convergence.