# A hybrid primal heuristic for Robust Multiperiod Network Design

## TL;DR

This paper introduces a hybrid primal heuristic combining ant colony optimization and exact large neighborhood search to effectively solve the complex Robust Multiperiod Network Design Problem, which accounts for multiple periods and traffic uncertainty.

## Contribution

The paper presents a novel hybrid heuristic approach that significantly improves solution quality for the challenging Robust Multiperiod Network Design Problem.

## Key findings

- Heuristic achieves low optimality gaps on realistic instances.
- Method outperforms existing approaches in solution quality.
- Effective handling of traffic uncertainty and multiple periods.

## Abstract

We investigate the Robust Multiperiod Network Design Problem, a generalization of the classical Capacitated Network Design Problem that additionally considers multiple design periods and provides solutions protected against traffic uncertainty. Given the intrinsic difficulty of the problem, which proves challenging even for state-of-the art commercial solvers, we propose a hybrid primal heuristic based on the combination of ant colony optimization and an exact large neighborhood search. Computational experiments on a set of realistic instances from the SNDlib show that our heuristic can find solutions of extremely good quality with low optimality gap.