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Integer programming models and branch-and-cut approaches to generalized {0,1,2}-survivable network design problems.

Markus Leitner¹

¹Department of Statistics and Operations Research, Faculty of Business, Economics and Statistics, University of Vienna, Vienna, Austria.

Computational Optimization and Applications

|August 12, 2016

Summary

This summary is machine-generated.

This study presents the Generalized k-Survivable Network Design Problem (k-GSNDP) for backbone networks. Two optimized branch-and-cut algorithms demonstrate superior performance for network design challenges.

Keywords:

Branch-and-cut Biconnectivity Generalized network design Mixed integer linear programming Survivability

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Area of Science:

Operations Research
Computer Science
Network Engineering

Background:

The design of robust backbone networks is critical for modern infrastructure.
Existing network design problems lack comprehensive survivability considerations.

Purpose of the Study:

Introduce the Generalized k-Survivable Network Design Problem (k-GSNDP).
Develop and compare mixed integer linear programming formulations for k-GSNDP.
Evaluate the efficiency of branch-and-cut algorithms for solving k-GSNDP.

Main Methods:

Formulating the k-GSNDP using mixed integer linear programming.
Developing branch-and-cut algorithms tailored for k-GSNDP.
Conducting extensive computational studies to compare algorithm variants.

Main Results:

Two specific k-GSNDP formulations and their corresponding branch-and-cut algorithms show significant advantages.
The study provides detailed insights into algorithm performance across various instance characteristics.
Identified optimal approaches for different network design scenarios.

Conclusions:

The proposed k-GSNDP framework enhances backbone network design.
The developed branch-and-cut algorithms offer efficient solutions for survivable network design.
Algorithmic choices should consider specific network instance properties for optimal results.