Computational risk management techniques for fixed charge network flow problems with uncertain arc failures

2011 ◽  
Vol 25 (1) ◽  
pp. 99-122 ◽  
Author(s):  
Alexey Sorokin ◽  
Vladimir Boginski ◽  
Artyom Nahapetyan ◽  
Panos M. Pardalos
Keyword(s):  
Risk Management ◽  
Network Flow ◽  
Fixed Charge ◽  
Network Flow Problems ◽  
Flow Problems ◽  
Management Techniques ◽  
Arc Failures

Bilinear modeling solution approach for fixed charge network flow problems

2009 ◽  
Vol 3 (3) ◽  
pp. 347-355 ◽  
Author(s):  
Steffen Rebennack ◽  
Artyom Nahapetyan ◽  
Panos M. Pardalos
Keyword(s):  
Network Flow ◽  
Fixed Charge ◽  
Network Flow Problems ◽  
Solution Approach ◽  
Flow Problems ◽  
Bilinear Modeling

Three-partition flow cover inequalities for constant capacity fixed-charge network flow problems

Networks ◽  
2016 ◽  
Vol 67 (4) ◽  
pp. 299-315 ◽  
Author(s):  
Alper Atamtürk ◽  
Andrés Gómez ◽  
Simge Küçükyavuz
Keyword(s):  
Network Flow ◽  
Fixed Charge ◽  
Network Flow Problems ◽  
Flow Problems ◽  
Cover Inequalities

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.

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