ABSTRACT
The outbreak of the COVID-19 epidemic has had a significant impact in increasing the number of emergency calls, which causes significant problems to emergency medical services centers (EMS) in many countries around the world, such as Saudi Arabia, which attracts a huge number of pilgrims during pilgrimage seasons. Among these issues, we address real-time ambulance dispatching and relocation problems (real-time ADRP). This paper proposes an improved MOEA/D algorithm using Simulated Annealing (G-MOEA/D-SA) to handle the real-time ADRP issue. The simulated annealing (SA) seeks to obtain optimal routes for ambulances to cover all emergency COVID-19 calls through the implementation of convergence indicator based dominance relation (CDR). To prevent the loss of good solutions once they are found in the G-MOEA/D-SA algorithm, we employ an external archive population to store the non-dominated solutions using the epsilon dominance relationship. Several experiments are conducted on real data collected from Saudi Arabia during the Covid-19 pandemic to compare our algorithm with three relevant state-of-art algorithms including MOEA/D, MOEA/D-M2M and NSGA-II. Statistical analysis of the comparative results obtained using ANOVA and Wilcoxon test demonstrate the merits and the outperformance of our G-MOEA/D-SA algorithm.
ABSTRACT
Due to the lack of medical materials in some emergency public events, for example, the outbreak of COVID-19, it is urgent to establish a medical emergency material warehouse. Taking Xi'an, China, as an example, this study aims to select suitable sites of Xi'an medical emergency material warehouse. In this study, the problem of site selection models as a multiobjective optimization problem. The coverage function and comprehensive efficiency function are designed as two conflicting objectives. Then, a multiobjective evolutionary algorithm based on multiple memetic direction is proposed to optimize the two objectives concurrently. The crossover and mutation operators are designed for evolutionary multiobjective site selection. The proposed crossover operator is able to balance the global and local search abilities, and the proposed mutation operator fuses the distribution information of hospital location, service population, and the overall coverage. Experiments on real dataset verify the superiority of the proposed evolutionary multiobjective site selection method.
ABSTRACT
Mathematical modeling can be utilized to find out how the coronavirus spreads within a population. Hence, considering models that can precisely describe natural phenomena is of crucial necessity. Besides, although one of the most significant benefits of mathematical modeling is designing optimal policies for battling the disease, there are a few studies that employ this beneficial aspect. To this end, this study aims to design optimal management policies for the novel coronavirus disease 2019 (COVID-19). This is a pioneering research that designs optimal policies based on multi-objective evolutionary algorithms for control of the fractional-order model of the COVID-19 outbreak. First, a fractional-order model of the disease dynamic is presented. The impacts of the fractional derivative's value on the modeling and forecasting of the disease spread are considered. After that, a multi-objective optimization problem is proposed by considering the rate of communication, the transition of symptomatic infected class to the quarantined one, and the release of quarantined uninfected individuals. Numerical results clearly corroborate that by solving the proposed multi-objective problem, governments can control the massive disease outbreak while economic factors have reasonable values that prevent economic collapse.