Research on the Allocation of 3D Printing Emergency Supplies in Public Health Emergencies

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.

Multiobjective Emergency Resource Allocation under the Natural Disaster Chain with Path Planning

“…In order to evaluate the fitness for multiobjective optimization, the chromosomes undergo rapid nondominated sorting [49]. The fitness of chromosome Fitness(C) is defined according to the layer and crowding distance.…”

Optimization of Multi-Objective Mobile Emergency Material Allocation for Sudden Disasters

“…The service points of mobile emergency facilities of n k oi = 1 are eligible to participate in the transfer of mobile emergency supplies. Based on the conservation of materials in constraint (3), the constraint (7) indicates that the sum of the maximum demand j d k j to meet any demand point should be less than the upper limit h maxk o at the mobile emergency supplies storage center. This is required to prevent the system model failure caused by the mismatching of the quantity of materials at the upper and lower levels.…”

“…Based on fairness, timeliness and economy, Bai ( 6 ) designed a multi-objective model for facility location, vehicle routing and material allocation decision problems. He et al ( 7 ) focused on 3D printing emergency supplies distribution to improve the shortage of post-disaster medical supplies, and to explore its supply-demand relationship and efficient distribution path. Pradhananga et al ( 8 ) demonstrated the multi-source emergency supplies that are an effective approach to reduce the social costs.…”

Large-Scale Customized Production Scheduling of Multiagent-Based Medical 3D Printing

“…M-3DP technology has the advantage of high digitization, rapid prototyping, and product customization [ 1 ], which is already applied to surgical planning, revision surgery, and other medical purposes [ 2 ]. With the frequent occurrence of major public health events in the world and the increasing demand for large-scale and customized production of medical products [ 3 ], M-3DP technology has been improved, while facing increased requirements placed on its large-scale customized production scheduling capability. However, few researchers have considered the product allocation and production scheduling in 3D printing (3DP) from the perspective of large-scale customized production and multi-printing materials.…”

“…Based on extensive literature research, it was found that because the intelligent optimization algorithm is not constrained by specific problems, it is widely used in the field of production workshop scheduling by imitating the biological evolution of nature [14][15][16]. Different intelligent optimization algorithms were adopted by researchers in the heuristic algorithms, including GA [3,7,11,17], particle swarm optimization (PSO) [17], and water wave optimization algorithm [16]. Moreover, some researchers have compared GA, PSO, and other heuristic algorithms and found that GA is widely used in 3D printing shop scheduling problems because of its good robustness and global optimization ability compared with other algorithms [17].…”