Optimal network-flow-distribution algorithms

1994 ◽  
Vol 29 (4) ◽  
pp. 490-499
Author(s):  
B. N. Pshenichnyi ◽  
E. E. Kirik
Keyword(s):  
Network Flow ◽  
Flow Distribution ◽  
Optimal Network ◽  
Distribution Algorithms

Application of the optimal network flow method to the problem of locomotive selection for freight trains on the Eastern Polygon

2019 ◽  
pp. 94-106
Author(s):  
Александр Борисович Шабунин ◽  
Андрей Куркенович Такмазьян
Keyword(s):  
Network Flow ◽  
Optimal Solution ◽  
Global Optimum ◽  
Full Scale ◽  
Scale Model ◽  
Test Cases ◽  
Processor Time ◽  
Flow Method ◽  
Optimal Network ◽  
Locomotive Assignment

Моделируется подбор тяговых ресурсов (локомотивов - в данном случае) для провоза грузовых поездов. В качестве входных данных рассматриваются маршрут поезда, время готовности поезда к отправлению, средняя скорость и вес поезда. Имеется множество локомотивов, обладающих грузоподъемностью и областью разрешенного действия. Цель - оптимально подобрать ресурс для каждого участка маршрута поезда. Решение ищется методом потока ресурсов минимальной суммарной стоимости через специально сконструированную сеть. Сеть построена на основе взвешенного орграфа из ребер графика поездов на линейных участках и ребер альтернативы, в процессе прохода по которым осуществляется “смена деятельности” локомотива (например, отцепление от одного поезда и подцепка к другому). Полученное решение обладает свойством глобальной оптимальности по времени. The selection of traction resources (locomotives) for the transport of freight trains is modelled. The input data are the train route, the readiness time of the train for departure, the average speed and weight of the train. In addition, there are many locomotives with a carrying capacity and an area of permitted action. The research objective is to optimally select a resource for each segment of the train route. The solution is sought by the resource flow method of the minimum total cost through a specially designed network. The network includes edges created from train schedule segments whose filling means locomotive assignment to train at the segment, and special alternative edges, passing through which a locomotive alternates its assignment. The algorithm for finding the optimal solution is the method of pushing through the pre-flow proposed by A. Goldberg and R. Tarjan. This is one of the fastest algorithms converging to a global optimum. Two test cases were investigated: a trivial one, out of six trains and three locomotives, and a more complicated one, which is a model example the size of 10% of the full scale model and consists of 150 trains. Full scale calculations provide planning of the freight transportation on the Eastern Operational domain of the Russian Railways. The model includes 1800 locomotives and about 3000 trains on the time horizon of 48 hours. Solution is found in less than 5 minutes of processor time for a PC powered by Intel(R) Pentium(R) G2010 2.80 GHz processor.

Analysis of network flow distribution: Computational aid to minimize experimental expenditure

1983 ◽  
Vol 26 (2) ◽  
pp. 221-237 ◽  
Author(s):  
Th. Wetter ◽  
D. Hoffmann ◽  
H. Schmid-Schönbein
Keyword(s):  
Network Flow ◽  
Flow Distribution

scholarly journals A Study on the Coordination of Urban Traffic Control and Traffic Assignment

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
ZhaoWei Qu ◽  
Yan Xing ◽  
XianMin Song ◽  
YuZhou Duan ◽  
Fulu Wei
Keyword(s):  
Traffic Control ◽  
Network Flow ◽  
Road Network ◽  
Traffic Assignment ◽  
Route Choice ◽  
Flow Distribution ◽  
Urban Traffic ◽  
Network Efficiency ◽  
The Road ◽  
Value Model

The interactions between signal setting and traffic assignment can directly affect the urban road network efficiency. In order to improve the coordination of signal setting with traffic assignment, this paper created a traffic control algorithm considering traffic assignment; meanwhile, the link impedance function and the route choice function were introduced into this paper to study the user's route choice and the road network flow distribution. Then based on the above research, we created a system utility value model. Finally through the VISSIM software to simulate the test network, we verified the superiority of the coordination algorithm and the model and gave the optimal flow of the road network.

scholarly journals Modernization of Todini Global Gradient Algorithm for hydraulic analysis of networks with choked flow

2020 ◽  
Vol 219 ◽  
pp. 01003
Author(s):  
Leonid Korelstein
Keyword(s):  
Network Flow ◽  
Flow Distribution ◽  
Gradient Algorithm ◽  
Hydraulic Analysis ◽  
Distribution Problem ◽  
Choked Flow ◽  
Global Gradient Algorithm ◽  
Hydraulic Network ◽  
Piping Networks ◽  
Gas Liquid Flow

The modification of well-known Global Gradient Algorithm for hydraulic network flow distribution problem is proposed. This modification is based on problem equations rewritten in “upstream” form and on modified form of linearization, and can be effectively used for piping networks with gas and multiphase gas-liquid flow with multiple choked flow.

Entransy analysis of secondary network flow distribution in absorption heat exchanger

Energy ◽  
2018 ◽  
Vol 147 ◽  
pp. 428-439 ◽  
Author(s):  
Xiaoyin Wang ◽  
Xiling Zhao ◽  
Lin Fu
Keyword(s):  
Heat Exchanger ◽  
Network Flow ◽  
Flow Distribution ◽  
Secondary Network

scholarly journals Assessing potential errors of MRI-based measurements of pulmonary blood flow using a detailed network flow model

2012 ◽  
Vol 113 (1) ◽  
pp. 130-141 ◽  
Author(s):  
K. S. Burrowes ◽  
R. B. Buxton ◽  
G. K. Prisk
Keyword(s):  
Blood Flow ◽  
Network Flow ◽  
Pulmonary Blood Flow ◽  
Sagittal Plane ◽  
Flow Distribution ◽  
Image Plane ◽  
Relative Dispersion ◽  
Plane Flow ◽  
Image Planes ◽  
Imaging Plane

MRI images of pulmonary blood flow using arterial spin labeling (ASL) measure the delivery of magnetically tagged blood to an image plane during one systolic ejection period. However, the method potentially suffers from two problems, each of which may depend on the imaging plane location: 1) the inversion plane is thicker than the imaging plane, resulting in a gap that blood must cross to be detected in the image; and 2) ASL includes signal contributions from tagged blood in conduit vessels (arterial and venous). By using an in silico model of the pulmonary circulation we found the gap reduced the ASL signal to 64–74% of that in the absence of a gap in the sagittal plane and 53–84% in the coronal. The contribution of the conduit vessels varied markedly as a function of image plane ranging from ∼90% of the overall signal in image planes that encompass the central hilar vessels to <20% in peripheral image planes. A threshold cutoff removing voxels with intensities >35% of maximum reduced the conduit vessel contribution to the total ASL signal to ∼20% on average; however, planes with large contributions from conduit vessels underestimate acinar flow due to a high proportion of in-plane flow, making ASL measurements of perfusion impractical. In other image planes, perfusion dominated the resulting ASL images with good agreement between ASL and acinar flow. Similarly, heterogeneity of the ASL signal as measured by relative dispersion is a reliable measure of heterogeneity of the acinar flow distribution in the same image planes.

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