On solving multi-commodity flow problems: An experimental evaluation

doi:10.1016/j.cja.2017.05.012

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On solving multi-commodity flow problems: An experimental evaluation

2017-08-01

Weibin Dai

张军 (Jun Zhang)

Xiaoqian Sun *

DOI：10.1016/j.cja.2017.05.012

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Abstract

En 中文

Multi-commodity flow problems (MCFs) can be found in many areas, such as transportation, communication, and logistics. Therefore, such problems have been studied by a multitude of researchers, and a variety of methods have been proposed for solving it. However, most researchers only discuss the properties of different models and algorithms without taking into account the impacts of actual implementation. In fact, the true performance of a method may differ greatly across various implementations. In this paper, several popular optimization solvers for implementations of column generation and Lagrangian relaxation are discussed. In order to test scalability and optimality, three groups of networks with different structures are used as case studies. Results show that column generation outperforms Lagrangian relaxation in most instances, but the latter is better suited to networks with a large number of commodities. (C) 2017 Chinese Society of Aeronautics and Astronautics. Production and hosting by Elsevier Ltd.

Keywords:

Multi-commodity flow problem

Column generation

Lagrangian relaxation

Evaluation

Implementation

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