Memory Chains for Optimizing the Table Disposition During the COVID-19 Pandemic

ABSTRACT

The coronavirus disease 2019 (COVID-19) pandemic has supposed a challenge for some economic sectors that have suffered preventive lockdowns during the last year for mitigating the virus propagation. Among them, hostelry is one of the most affected sectors, especially indoor establishments in which the contagion probability significantly increases. In this context, preserving the interpersonal distance while wearing facemasks in these establishments has been demonstrated as a key factor to control the virus propagation in hostelry environments. The achievement of this objective entails the addressing of the Table Location Problem (TLP) which allows the maximization of the distance among the tables of a particular establishment. The TLP is considered as NP-Hard suggesting the application of metaheuristics to achieve competitive results in acceptable times. In this paper we propose a novel algorithm for the TLP (MA-GB-Chains) based on memory chains to select the more promising individuals for applying a local search procedure to introduce knowledge during the optimization process. This algorithm has been proved in a real hostelry environment reaching improved results to previous approaches to the TLP thus fulfilling the main objectives of this paper. © 2021, Springer Nature Switzerland AG.