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Related Experiment Video

Updated: Sep 4, 2025

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Robust min-max regret covering problems.

Amadeu A Coco¹, Andréa Cynthia Santos¹, Thiago F Noronha²

¹UNIHAVRE, UNIROUEN, INSA Rouen, LITIS, Normandie Université, Le Havre, France.

Computational Optimization and Applications

|July 20, 2022

Summary

This summary is machine-generated.

This study introduces robust optimization counterparts for set covering problems, developing algorithms for min-max regret Weighted Set Covering Problem and Maximum Benefit Set Covering Problem under data uncertainty.

Keywords:

Covering problems Exact methods Heuristics Robust optimization Uncertainties

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Area of Science:

Operations Research
Optimization
Computer Science

Background:

Addresses uncertainty in data by modeling parameters as continuous intervals.
Focuses on robust optimization counterparts of Weighted Set Covering Problem (WSCP) and Maximum Benefit Set Covering Problem (MSCP).

Purpose of the Study:

To introduce and analyze min-max regret versions of WSCP and MSCP.
To develop and evaluate exact and heuristic algorithms for these robust covering problems.

Main Methods:

Proves MSCP is NP-Hard.
Develops a mathematical formulation for min-max regret MSCP.
Adapts and applies exact algorithms (Logic-based Benders decomposition, extended Benders decomposition, branch-and-cut) and five heuristic algorithms (scenario-based, path relinking, pilot method, LP-based).

Main Results:

Demonstrates the NP-Hardness of MSCP.
Provides a mathematical formulation for the min-max regret MSCP.
Evaluates the performance and solution quality of various exact and heuristic algorithms on robust covering problems.

Conclusions:

The study contributes novel algorithms and formulations for robust set covering problems.
The research analyzes the effectiveness of different algorithmic approaches in handling data uncertainty.
Findings offer insights into solving real-world problems with interval uncertainty.