A hybrid variable neighborhood search algorithm for targeted offers in direct marketing.

This paper focuses on the targeted offers problem in direct marketing campaigns. The main objective is to maximize the feedback of customers purchases, offering products for the set of customers with the highest probability of positively accepting the offer and, at the same time, minimizing the operational costs of the campaign. Given the combinatorial nature of the problem and the large volume of data, involving instances with up to one million customers, approaches solely based on mathematical programming methods, said exact, appear limited and infeasible. In this paper, the use of a hybrid heuristic algorithm, based on the Greedy Randomized Adaptive Search Procedures and General Variable Neighborhood Search, is proposed. Computational experiments performed on a set of test problems from the literature show that the proposed algorithm was able to produce competitive solutions