Generic Pareto local search metaheuristic for optimization of targeted offers in a bi-objective direct marketing campaign

Autor: Frederico Gadelha Guimarães, Thibaut Lust, Peter J. Fleming, Helena Ramalhinho, Vitor Nazário Coelho, Igor Machado Coelho, El-Ghazali Talbi, Thays A. Oliveira, Marcone Jamilson Freitas Souza, Bruno Nazário Coelho
Přispěvatelé: Departamento de Engenharia Elétrica [Minas Gerais] (DEE - UFMG), Universidade Federal de Minas Gerais, Universitat Pompeu Fabra [Barcelona] (UPF), Universidade do Estado do Rio de Janeiro [Rio de Janeiro] (UERJ), Universidade Federal de Ouro Preto (UFOP), Department of Automatic Control and Systems Engineering [ Sheffield] (ACSE), University of Sheffield [Sheffield], Universidade Federal de Minas Gerais [Belo Horizonte] (UFMG), Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Parallel Cooperative Multi-criteria Optimization (DOLPHIN), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), DECISION, Laboratoire d'Informatique de Paris 6 (LIP6), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)
Jazyk: angličtina
Rok vydání: 2017
Předmět:
Zdroj: Computers and Operations Research
Computers and Operations Research, 2017, 78, pp.578-587. ⟨10.1016/j.cor.2016.09.008⟩
Computers and Operations Research, Elsevier, 2017, 78, pp.578-587. ⟨10.1016/j.cor.2016.09.008⟩
ISSN: 0305-0548
1873-765X
Popis: 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. The authors would like to thank Brazilian agency CAPES, CNPq (grants 305506/2010-2, 552289/2011-6, 306694/2013-1 and 312276/2013-3), FAPEMIG (grants APQ-04611-10, PPM CEX497-13) and FP7 CORDIS, “New Horizons for Multi Criteria Decision Making”, for supporting the development of this work. Helena Ramalhinho was also partially supported by the Spanish Ministry of Economy and Competitiveness (TRA2013- 48180-C3-P, TRA2015-71883-REDT).
Databáze: OpenAIRE
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