Please use this identifier to cite or link to this item: http://www.repositorio.ufop.br/handle/123456789/9371
Title: Generic Pareto local search metaheuristic for optimization of targeted offers in a bi-objective direct marketing campaign.
Authors: 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
Keywords: Direct marketing campaign
Sharpe ratio
Cross-selling
Metaheuristics
Issue Date: 2016
Citation: COELHO, V. N. et al. Generic Pareto local search metaheuristic for optimization of targeted offers in a bi-objective direct marketing campaign. Computers & Operations Research, v. 78, p. 578-587,  2016. Disponível em: <https://www.sciencedirect.com/science/article/pii/S0305054816302283>. Acesso em: 16 jan. 2018.
Abstract: 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.
URI: http://www.repositorio.ufop.br/handle/123456789/9371
metadata.dc.identifier.uri2: https://www.sciencedirect.com/science/article/pii/S0305054816302283
ISSN: 03050548
Appears in Collections:DECOM - Artigos publicados em periódicos

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