Algorithms for the minimum sum coloring problem: a review

TL;DR

This review comprehensively covers recent algorithms for the Minimum Sum Coloring Problem, highlighting advances in approximation and practical solution methods since 2004, and aims to guide future research and applications.

Contribution

It provides a detailed classification and analysis of recent MSCP algorithms, emphasizing their frameworks and success factors, which was lacking in prior reviews.

Findings

Significant progress in approximation algorithms for MSCP

Development of practical solution algorithms with improved performance

Identification of key components contributing to algorithm success

Abstract

The Minimum Sum Coloring Problem (MSCP) is a variant of the well-known vertex coloring problem which has a number of AI related applications. Due to its theoretical and practical relevance, MSCP attracts increasing attention. The only existing review on the problem dates back to 2004 and mainly covers the history of MSCP and theoretical developments on specific graphs. In recent years, the field has witnessed significant progresses on approximation algorithms and practical solution algorithms. The purpose of this review is to provide a comprehensive inspection of the most recent and representative MSCP algorithms. To be informative, we identify the general framework followed by practical solution algorithms and the key ingredients that make them successful. By classifying the main search strategies and putting forward the critical elements of the reviewed methods, we wish to encourage future development of more powerful methods and motivate new applications.