Navegando por Autor "Ramalhinho, Helena"

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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.
A VNS approach for book marketing campaigns generated with quasi-bicliques probabilities.
(2017) Oliveira, Thays Aparecida de; Coelho, Vitor Nazário; Ramalhinho, Helena; Souza, Marcone Jamilson Freitas; Coelho, Bruno Nazário; Rezende, Daniel C.; Coelho, Igor Machado
This paper focuses on Book Marketing Campaigns, where the benefit of offering each book is calculated based on a bipartite graph (biclique). A quasi Biclique problem is assessed for obtaining the probabilities of success of a given client buy a given book, considering it had received another book as free offer. The remaining optimization decision problem can be solved following the Targeted Offers Problem in Direct Marketing Campaigns. The main objective is to maximize the feedback of customers purchases, offering books to the set of customers with the highest probability of buying others ones from its biclique and, at the same time, minimizing campaign operational costs. Given the combinatorial nature of the problem and the large volume of data, which can involve real cases with up to one million customers, metaheuristics procedures have been used as an efficient way for solving it. Here, a hybrid trajectory search based algorithm, namely GGVNS, which combines the Greedy Randomized Adaptive Search Procedures and General Variable Neighborhood Search, is used. The strategy for generating the quasi Biclique problem is described and a new instance generator for the TOPDMC is introduced. Computational results regarding the GGVNS algorithm shows it is able to find useful and profitable sets of clients.