Solving the shopping plan problem through bio-inspired approaches

Blended commerce involves all commerce experiences in which customers make use of different channels (online, offline and mobile) for their purchases to take advantages with respect to their needs and attitudes. This new e-commerce trend is typically characterized by so-called loyalty programmes such as coupons and system points. These mechanisms can be extremely useful for the companies to achieve customer retention and for the customers to obtain discounts. However, loyalty programmes can complicate for customers the evaluation of all offers and the selection of optimal providers (shopping plan) for buying the desired set of products. To face this problem, referred as Shopping Plan Problem, optimization algorithms are emerging as a suitable methodology. This paper is aimed at performing a systematic comparison amongst three bio-inspired optimization approaches, genetic algorithms, memetic ones and ant colony optimization, to detect the best performer for solving the shopping plan problem in a blended shopping scenario. © 2015 Springer-Verlag Berlin Heidelberg

Blended commerce involves all commerce experiences in which customers make use of different channels (online, offline and mobile) for their purchases to take advantages with respect to their needs and attitudes. This new e-commerce trend is typically characterized by so-called loyalty programmes such as coupons and system points. These mechanisms can be extremely useful for the companies to achieve customer retention and for the customers to obtain discounts. However, loyalty programmes can complicate for customers the evaluation of all offers and the selection of optimal providers (shopping plan) for buying the desired set of products. To face this problem, referred as Shopping Plan Problem, optimization algorithms are emerging as a suitable methodology. This paper is aimed at performing a systematic comparison amongst three bio-inspired optimization approaches, genetic algorithms, memetic ones and ant colony optimization, to detect the best performer for solving the shopping plan problem in a blended shopping scenario.