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Related Experiment Videos

A deterministic annealing algorithm for the minimum concave cost network flow problem.

Chuangyin Dang¹, Yabin Sun, Yuping Wang

¹Department of Manufacturing Engineering & Engineering Management, City University of Hong Kong, Hong Kong, China. mecdang@cityu.edu.hk

Neural Networks : the Official Journal of the International Neural Network Society

|April 13, 2011

Summary

This summary is machine-generated.

A new deterministic annealing algorithm efficiently solves minimum concave cost network flow problems, including single- and multiple-source variations, particularly those with dense arcs. This method offers a unified approach for complex network flow challenges.

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Area of Science:

Operations Research
Computer Science

Background:

Existing algorithms for minimum concave cost network flow problems primarily address single-source scenarios.
A need exists for a unified approach to handle both single-source and multiple-source problems, especially those with dense arcs.

Purpose of the Study:

To propose a novel deterministic annealing algorithm for minimum concave cost network flow problems.
To effectively address both single-source and multiple-source capacitated problems, with a focus on dense arc configurations.

Main Methods:

The algorithm is derived from the application of Lagrange and Hopfield-type barrier functions.
It involves updating Lagrange multipliers via a globally convergent iterative procedure to find a feasible descent direction.
A second step generates a point in the descent direction, ensuring automatic satisfaction of variable bounds.

Main Results:

The proposed algorithm is applicable to both single-source and multiple-source capacitated network flow problems.
It demonstrates particular effectiveness and efficiency for problems characterized by dense arcs.
Numerical results from 48 test problems confirm the algorithm's efficacy and efficiency.

Conclusions:

The deterministic annealing algorithm provides an effective and efficient solution for minimum concave cost network flow problems.
The method offers a unified framework for single- and multiple-source problems, excelling with dense arc instances.