## 7 Conclusions  
Increasing levels of competitiveness among brands as well as among channels of the same brand make it difficult for retailers in brick-and-mortar stores to engage customers while in the shop. One of the ways to attract them to the stores is to offer a different experience and a factor of surprise. Displaying a set of correlated and attractive products on retail display tables that vary often is a promising way to engage customers with a pleasant experience. From a managerial perspective, being able to know the selection of products that maximizes the attractiveness level enables a rationalization of the stock available in the store. Moreover, reducing the time required to make these decisions might significantly increase the productivity of the managers in charge of them.  
In this paper, we propose a rich and realistic multi-period product display problem, as well as biased-randomized algorithms that allow us to solve it in an efficient way. In the considered problem, a set of correlated products has to be selected over multiple periods of time in order to maximize the total attractiveness level of the display tables in a retail store. A number of realistic characteristics and constraints have been incorporated in the problem to increase the potential applications of our work. Some of these are: (i) the inclusion of both expensive and non-expensive products on each display table and horizon; (ii) the achievement of a minimum profit margin per table and horizon; (iii) the consideration of dynamic (novelty-based) and correlated (combination-based) attractiveness levels; and (iv) the consideration of dynamic selling prices.  
As solution approaches, a biased-randomized GRASP and a biased-randomized ILS have been proposed. To test these methodologies, a complete set of instances was generated by considering realistic assumptions and different design factors. In our approach, it is assumed that the attractiveness value of each product can be estimated using historical data obtained from an omnichannel environment. The experimental results show that both biased-randomized methodologies are able to provide, in short computing times, solutions that clearly outperform the human-behavior and other more standard methodologies. Additionally, a numerical study has shown that our biased-randomized algorithms are very competitive when compared with non-linear solver engines, obtaining better or similar solutions in much shorter computing times.  
By increasing the attractiveness level of retail display tables in a short time horizon, managers can reduce customer attrition and, as a consequence, increase sales revenue in their stores. Using biased-randomized algorithms to maximize the attractiveness of products assigned to display tables in the considered scenario represents a clear enhancement over current practice, which might typically require many hours of a dedicated expert to generate even a feasible solution.  
**Author Contributions:** conceptualization, M.M. and A.A.J.; methodology and software, L.d.C.M. and S.H.; writing, M.M., L.d.C.M.; validation, V.F. All authors have read and agree to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Acknowledgments:** This work has been partially supported by the Spanish Ministry of Science, Innovation, and Universities (RED2018-102642-T) and the Erasmus+ programme (2019-I-ES01-KA103-062602).  
**Conflicts of Interest:** The authors declare no conflict of interest.