ABSTRACT
The coronavirus disease (COVID-19) pandemic has challenged multiple aspects of our lives. Social distancing among other preventive measures for reducing the contagion probability have supposed a significant challenge for many establishments. Restaurants, schools, conferences are establishments founded by the congregation of participants, distributed in tables or chairs over a certain scenario. These enterprises now face an optimization problem in their daily routine, where they seek to maximize the interpersonal distance while also allocating the maximum number of assistants. The optimization of these distribution paradigms, such as the CLP (Chair Location Problem), has been defined as NP-Hard, therefore, the use of metaheuristic techniques, such as Genetic Algorithms is recommended for obtaining an optimal solution within a polynomial time. In this paper, a GA is proposed for solving the CLP, attaining an optimal solution that maximizes the interpersonal distance among assistants while also guaranteeing a minimum distance separation for reducing the contagion probability. Results of the proposed methodology and multiple fitness evaluation strategies prove its viability for attaining a valid distribution for these establishments, thus satisfying the main objectives of this research. © 2022, Springer Nature Switzerland AG.
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.