An experimental evaluation of interval-based adaptations of the Jonker–Volgenant algorithm

A. Rodríguez Martínez, A. Bouchet, S. Montes

This work investigates the impact of uncertainty in the Linear Assignment Problem through an experimental comparison between the classical Jonker–Volgenant algorithm and an interval-based extension. While the classical approach assumes precise cost values, many real-world applications involve imprecise or variable data that cannot be adequately represented by single numerical values.

The study evaluates both methods under different uncertainty scenarios, including deterministic, low-variability, and high-variability settings, focusing on robustness, stability, and solution quality. The results indicate that the use of interval costs provides a more expressive representation of uncertainty, leading to more robust and informative assignment solutions.

These findings suggest that interval-based optimization constitutes a valuable alternative to deterministic approaches, particularly in decision-making contexts where data uncertainty plays a significant role.

Palabras clave: Linear Assignment Problem, Jonker–Volgenant algorithm, interval data, uncertainty, optimization, decision-making

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