Abstract
Significant public health emergencies greatly impact the global supply chain system of production and cause severe shortages in personal protective and medical emergency supplies. Thus, rapid manufacturing, scattered distribution, high design degrees of freedom, and the advantages of the low threshold of 3D printing can play important roles in the production of emergency supplies. In order to better realize the efficient distribution of 3D printing emergency supplies, this paper studies the relationship between supply and demand of 3D printing equipment and emergency supplies produced by 3D printing technology after public health emergencies. First, we fully consider the heterogeneity of user orders, 3D printing equipment resources, and the characteristics of diverse production objectives in the context of the emergent public health environment. The multi-objective optimization model for the production of 3D printing emergency supplies, which was evaluated by multiple manufacturers and in multiple disaster sites, can maximize time and cost benefits of the 3D printing of emergency supplies. Then, an improved non-dominated sorting genetic algorithm (NSGA-II) to solve the multi-objective optimization model is developed and compared with the traditional NSGA-II algorithm analysis. It contains more than one solution in the Pareto optimal solution set. Finally, the effectiveness of 3D printing is verified by numerical simulation, and it is found that it can solve the matching problem of supply and demand of 3D printing emergency supplies in public health emergencies.
Highlights
In recent years, there has been a series of public health emergencies around the world, such as SARS, influenza A (H1N1), Escherichia coli outbreaks in Europe, Ebola in West Africa, and COVID-19
(1) In terms of the convergence of the algorithm, it can be seen from Table 5 that the average cost calculated by the improved NSGA-II algorithm is lower than that of the traditional NSGAII algorithm, that is, the improved NSGA-II algorithm can obtain a solution with higher user satisfaction at the same cost
The Pareto frontier obtained by the improved NSGA-II algorithm is better, and its convergence is better than that of the traditional NSGA-II algorithm
Summary
There has been a series of public health emergencies around the world, such as SARS, influenza A (H1N1), Escherichia coli outbreaks in Europe, Ebola in West Africa, and COVID-19. Ekici et al [16] established a combination optimization model of epidemic spread and food distribution location selection for the scheduling of various emergency resources, and designed a heuristic algorithm to solve large-scale practical problems. How to match 3D printing emergency supplies with 3D printing equipment for production and maximize the cost and time benefit of disaster-affected users has become an urgent problem to be solved. The remainder of this article is organized as follows: the second part discusses the 3D-printed emergency material scheduling model in the context of public health emergencies; the third part provides an improved NSGA-II algorithm; in the fourth part, an example is given to verify the effectiveness of the proposed method; the fifth part provides the conclusion and briefly discusses further research work.
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