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Vehicle routing for the urgent delivery of face shields during the COVID-19 pandemic. 新冠肺炎大流行期间紧急运送面罩的车辆路线
IF 1.1 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2020-01-01 Epub Date: 2020-08-18 DOI: 10.1007/s10732-020-09456-8
Joaquín Pacheco, Manuel Laguna

The speed by which the COVID-19 pandemic spread throughout the world caught some national and local governments unprepared. Healthcare systems found themselves struggling to increase capacity and procure key supplies, such as personal protective equipment. Protective face shields became essential for healthcare professionals. However, most hospitals and healthcare facilities did not have them in adequate quantities. The urgency of producing and delivering face shields increased as the number of COVID-19 cases rapidly multiplied. This was the situation that we encountered in the city and province of Burgos (Spain). Since there was no time to wait for a large manufacturer to produce face shields, private citizens and small companies volunteered to make them using technologies such as 3D printers. Nonprofits, citizens, and governments agencies volunteered to deliver materials to the face shield makers and to pick up and deliver the face shields to health centers and other locations where they were needed. This resulted in a vehicle routing problem with some special characteristics that made it different from models used for commercial purposes. We describe the development of a heuristic to find feasible and efficient routes for this problem. We highlight the advantages of using heuristics in an emergency context like the one triggered by the COVID-19 pandemic. In particular, the heuristic approach allowed us to design, implement, test, and delivery a routing system in less than 1 week from the time that the local government contacted us with what they described as a logistics nightmare.

COVID-19大流行在全球蔓延的速度让一些国家和地方政府措手不及。卫生保健系统发现自己在努力提高能力和采购个人防护装备等关键物资。防护面罩对医疗保健专业人员来说是必不可少的。但是,大多数医院和保健设施没有足够的数量。随着COVID-19病例数量迅速增加,生产和交付面罩的紧迫性也在增加。这就是我们在布尔戈斯市和省(西班牙)遇到的情况。由于没有时间等待大型制造商生产面罩,私人公民和小公司自愿使用3D打印机等技术制造面罩。非营利组织、公民和政府机构自愿向口罩制造商提供材料,并将口罩捡起来送到医疗中心和其他需要口罩的地方。这导致车辆路线问题具有一些特殊的特征,使其不同于用于商业目的的模型。我们描述了一个启发式的发展,以找到可行的和有效的路线,为这个问题。我们强调在COVID-19大流行引发的紧急情况下使用启发式方法的优势。特别是,启发式方法使我们能够在不到一周的时间内设计、实施、测试和交付一个路由系统,从当地政府联系我们到他们所说的物流噩梦。
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引用次数: 0
A hybrid genetic algorithm for the traveling salesman problem with drone 无人机旅行商问题的混合遗传算法
IF 2.7 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2019-11-13 DOI: 10.1007/s10732-019-09431-y
Quang Minh Ha, Y. Deville, Quang Dung Pham, Minh Hoàng Hà
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引用次数: 95
Heuristics for a vehicle routing problem with information collection in wireless networks 无线网络中具有信息收集的车辆路由问题的启发式算法
IF 2.7 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2019-10-24 DOI: 10.1007/s10732-019-09429-6
Luis Ernesto Flores Luyo, A. Agra, R. Figueiredo, Eladio Ocaña Anaya
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引用次数: 2
Focus distance-aware lifetime maximization of video camera-based wireless sensor networks 基于视频摄像机的无线传感器网络的焦距感知寿命最大化
IF 2.7 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2019-09-27 DOI: 10.1007/s10732-019-09428-7
A. Rossi, Ashutosh Kumar Singh, M. Sevaux
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引用次数: 5
A comparative study of multi-objective machine reassignment algorithms for data centres 数据中心多目标机器再分配算法的比较研究
IF 2.7 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2019-09-20 DOI: 10.1007/s10732-019-09427-8
Takfarinas Saber, X. Gandibleux, Michael O’Neill, Liam Murphy, Anthony Ventresque
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引用次数: 7
Hybrid adaptive large neighborhood search algorithm for the mixed fleet heterogeneous dial-a-ride problem 混合车队异构叫车问题的混合自适应大邻域搜索算法
IF 2.7 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2019-09-09 DOI: 10.1007/s10732-019-09424-x
M. Masmoudi, M. Hosny, E. Demir, E. Pesch
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引用次数: 22
The vehicle routing problem with cross-docking and resource constraints 具有交叉停靠和资源约束的车辆路径问题
IF 2.7 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2019-08-29 DOI: 10.1007/s10732-019-09423-y
P. Grangier, M. Gendreau, Fabien Lehuédé, Louis-Martin Rousseau
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引用次数: 19
Sample size calculations for the experimental comparison of multiple algorithms on multiple problem instances 用于在多个问题实例上对多种算法进行实验比较的样本量计算
IF 2.7 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2019-08-05 DOI: 10.1007/s10732-020-09454-w
F. Campelo, E. Wanner
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引用次数: 10
A coordinated production and transportation scheduling problem with minimum sum of order delivery times 以最小订单交付时间协调生产和运输调度问题
IF 2.7 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2019-07-25 DOI: 10.1007/s10732-019-09420-1
Ling Liu, Wenli Li, Kunpeng Li, X. Zou
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引用次数: 16
Predicting effective control parameters for differential evolution using cluster analysis of objective function features 利用目标函数特征聚类分析预测差分进化的有效控制参数
IF 2.7 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2019-06-20 DOI: 10.1007/s10732-019-09419-8
S. P. Walton, M. R. Brown
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引用次数: 4
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Journal of Heuristics
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