scholarly journals Research on configuration of railway self-equipped tanker based on minimum cost maximum flow model

2017 ◽  
Author(s):  
Yuefang Yang ◽  
Chunhui Gan ◽  
Tingting Shen
Keyword(s):  
Flow Model ◽  
Minimum Cost ◽  
Maximum Flow

scholarly journals Optimization and Planning of Emergency Evacuation Routes Considering Traffic Control

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Guo Li ◽  
Lijun Zhang ◽  
Zhaohua Wang
Keyword(s):  
Traffic Control ◽  
Flow Model ◽  
Evaluation Model ◽  
Minimum Cost ◽  
Maximum Flow ◽  
Emergency Evacuation ◽  
Control Measures ◽  
Source Node ◽  
Evacuation Plans ◽  
Evacuation Routes

Emergencies, especially major ones, happen fast, randomly, as well as unpredictably, and generally will bring great harm to people’s life and the economy. Therefore, governments and lots of professionals devote themselves to taking effective measures and providing optimal evacuation plans. This paper establishes two different emergency evacuation models on the basis of the maximum flow model (MFM) and the minimum-cost maximum flow model (MC-MFM), and proposes corresponding algorithms for the evacuation from one source node to one designated destination (one-to-one evacuation). Ulteriorly, we extend our evaluation model from one source node to many designated destinations (one-to-many evacuation). At last, we make case analysis of evacuation optimization and planning in Beijing, and obtain the desired evacuation routes and effective traffic control measures from the perspective of sufficiency and practicability. Both analytical and numerical results support that our models are feasible and practical.

scholarly journals Adaptation of Random Binomial Graphs for Testing Network Flow Problems Algorithms

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1716
Author(s):  
Adrian Marius Deaconu ◽  
Delia Spridon
Keyword(s):  
Network Flow ◽  
Large Scale ◽  
Random Network ◽  
Minimum Cost ◽  
Maximum Flow ◽  
Network Flow Problems ◽  
Commodity Flow ◽  
Vast Number ◽  
Flow Problems ◽  
Execution Speed

Algorithms for network flow problems, such as maximum flow, minimum cost flow, and multi-commodity flow problems, are continuously developed and improved, and so, random network generators become indispensable to simulate the functionality and to test the correctness and the execution speed of these algorithms. For this purpose, in this paper, the well-known Erdős–Rényi model is adapted to generate random flow (transportation) networks. The developed algorithm is fast and based on the natural property of the flow that can be decomposed into directed elementary s-t paths and cycles. So, the proposed algorithm can be used to quickly build a vast number of networks as well as large-scale networks especially designed for s-t flows.

scholarly journals A Generalized Minimum Cost Flow Model for Multiple Emergency Flow Routing

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Jianxun Cui ◽  
Shi An ◽  
Meng Zhao
Keyword(s):  
Flow Model ◽  
Time Window ◽  
Programming Model ◽  
Real Life ◽  
Minimum Cost ◽  
Emergency Evacuation ◽  
Mixed Integer ◽  
Fixed Cost ◽  
Minimum Cost Flow ◽  
Cost Flow

During real-life disasters, that is, earthquakes, floods, terrorist attacks, and other unexpected events, emergency evacuation and rescue are two primary operations that can save the lives and property of the affected population. It is unavoidable that evacuation flow and rescue flow will conflict with each other on the same spatial road network and within the same time window. Therefore, we propose a novel generalized minimum cost flow model to optimize the distribution pattern of these two types of flow on the same network by introducing the conflict cost. The travel time on each link is assumed to be subject to a bureau of public road (BPR) function rather than a fixed cost. Additionally, we integrate contraflow operations into this model to redesign the network shared by those two types of flow. A nonconvex mixed-integer nonlinear programming model with bilinear, fractional, and power components is constructed, and GAMS/BARON is used to solve this programming model. A case study is conducted in the downtown area of Harbin city in China to verify the efficiency of proposed model, and several helpful findings and managerial insights are also presented.

Minimum cost maximum flow algorithm for upstream bandwidth allocation in OFDMA passive optical networks

2015 ◽  
Vol 356 ◽  
pp. 103-108 ◽  
Author(s):  
Yating Wu ◽  
Bin Kuang ◽  
Tao Wang ◽  
Qianwu Zhang ◽  
Min Wang
Keyword(s):  
Optical Networks ◽  
Bandwidth Allocation ◽  
Minimum Cost ◽  
Maximum Flow ◽  
Passive Optical Networks ◽  
Flow Algorithm

The Minimum Cost Flow Problem of Uncertain Random Network

2018 ◽  
Vol 35 (03) ◽  
pp. 1850016
Author(s):  
Soheila Abdi ◽  
Fahimeh Baroughi ◽  
Behrooz Alizadeh
Keyword(s):  
Flow Model ◽  
Flow Problem ◽  
Random Network ◽  
Minimum Cost ◽  
Random Variable ◽  
Minimum Cost Flow ◽  
Feasible Set ◽  
Uncertain Random Network ◽  
Minimum Cost Flow Problem ◽  
Cost Flow

The aim of this paper is to present a novel method for solving the minimum cost flow problem on networks with uncertain-random capacities and costs. The objective function of this problem is an uncertain random variable and the constraints of the problem do not make a deterministic feasible set. Under the framework of uncertain random programming, a corresponding [Formula: see text]-minimum cost flow model with a prespecified confidence level [Formula: see text], is formulated and its main properties are analyzed. It is proven that there exists an equivalence relationship between this model and the classical deterministic minimum cost flow model. Then an algorithm is proposed to find the maximum amount of [Formula: see text] such that for it, the feasible set of [Formula: see text]-minimum cost flow model is nonempty. Finally, a numerical example is presented to illustrate the efficiency of our proposed method.

scholarly journals Optimized Spatiotemporal Data Scheduling Based on Maximum Flow for Multilevel Visualization Tasks

2020 ◽  
Vol 9 (9) ◽  
pp. 518
Author(s):  
Qing Zhu ◽  
Meite Chen ◽  
Bin Feng ◽  
Yan Zhou ◽  
Maosu Li ◽  
...  
Keyword(s):  
Flow Model ◽  
Data Flow ◽  
Scheduling Algorithm ◽  
Maximum Flow ◽  
Data Access ◽  
Spatiotemporal Data ◽  
Cloud Environment ◽  
Calculation Algorithm ◽  
Data Scheduling ◽  
First Come First Serve

Massive spatiotemporal data scheduling in a cloud environment play a significant role in real-time visualization. Existing methods focus on preloading, prefetching, multithread processing and multilevel cache collaboration, which waste hardware resources and cannot fully meet the different scheduling requirements of diversified tasks. This paper proposes an optimized spatiotemporal data scheduling method based on maximum flow for multilevel visualization tasks. First, the spatiotemporal data scheduling framework is designed based on the analysis of three levels of visualization tasks. Second, the maximum flow model is introduced to construct the spatiotemporal data scheduling topological network, and the calculation algorithm of the maximum data flow is presented in detail. Third, according to the change in the data access hotspot, the adaptive caching algorithm and maximum flow model parameter switching strategy are devised to achieve task-driven spatiotemporal data optimization scheduling. Compared with two typical methods of first come first serve (FCFS) and priority scheduling algorithm (PSA) by simulating visualization tasks at three levels, the proposed maximum flow scheduling (MFS) method has been proven to be more flexible and efficient in adjusting each spatiotemporal data flow type as needed, and the method realizes spatiotemporal data flow global optimization under limited hardware resources in the cloud environment.

Target Controllability in Multilayer Networks via Minimum-Cost Maximum-Flow Method

2020 ◽  
pp. 1-14
Author(s):  
Jie Ding ◽  
Changyun Wen ◽  
Guoqi Li ◽  
Pengfei Tu ◽  
Dongxu Ji ◽  
...  
Keyword(s):  
Minimum Cost ◽  
Maximum Flow ◽  
Multilayer Networks ◽  
Flow Method
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