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A construction heuristic for the capacitated Steiner tree problem.

Simon Van den Eynde1, Pieter Audenaert1, Didier Colle1

  • 1IDLab, Ghent University - imec, Ghent, Belgium.

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Summary
This summary is machine-generated.

This study introduces the Capacitated Shortest Path Heuristic (CSPH) to efficiently solve large-scale telecommunication network problems. CSPH offers a faster alternative to traditional methods for the capacitated Steiner Tree Problem (STP).

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Area of Science:

  • Operations Research
  • Telecommunications Engineering
  • Computer Science

Background:

  • Many real-world problems, including Fiber-To-The-Home (FTTH) network design, are modeled as variants of the Minimum Steiner Tree Problem (STP).
  • Traditional methods like integer linear programming struggle with the large graph sizes (over 20,000 nodes) encountered in some geographical areas.
  • Capacitated STP variants are crucial for cost-efficient fiber installment planning.

Purpose of the Study:

  • To present a novel heuristic algorithm for solving the capacitated Steiner Tree Problem (STP).
  • To address the computational limitations of existing methods for large-scale telecommunication network optimization.

Main Methods:

  • Extension of the shortest path heuristic for the STP.
  • Development of the Capacitated Shortest Path Heuristic (CSPH).
  • Implementation and experimental evaluation on realistic telecommunications datasets.

Main Results:

  • CSPH demonstrates efficient performance, solving large graphs (e.g., 50,000 nodes) in under a minute.
  • The heuristic achieves an average time complexity of O(|V|^2), quadratic in the number of nodes.
  • CSPH provides a viable alternative for problems previously unattainable by integer linear programming.

Conclusions:

  • The Capacitated Shortest Path Heuristic (CSPH) is a practical and scalable solution for capacitated STP in telecommunications.
  • CSPH's straightforward implementation allows for further extensions and adaptations.
  • This heuristic significantly improves the feasibility of optimizing large-scale fiber optic network deployments.