A Survey on Parallel Genetic Algorithms for Shop Scheduling Problems

TL;DR

This survey reviews recent developments in parallel genetic algorithms for shop scheduling problems, highlighting their design frameworks and the impact of high-performance computing on solving NP-hard scheduling issues.

Contribution

It provides a comprehensive overview and categorization of recent parallel GA approaches for shop scheduling, emphasizing their design frameworks and computational efficiencies.

Findings

Parallel GAs significantly reduce solution times for complex scheduling problems.

Categorization of parallel GAs helps in understanding different design strategies.

High-performance computing enhances the scalability of parallel GAs.

Abstract

There have been extensive works dealing with genetic algorithms (GAs) for seeking optimal solutions of shop scheduling problems. Due to the NP hardness, the time cost is always heavy. With the development of high performance computing (HPC) in last decades, the interest has been focused on parallel GAs for shop scheduling problems. In this paper, we present the state of the art with respect to the recent works on solving shop scheduling problems using parallel GAs. It showcases the most representative publications in this field by the categorization of parallel GAs and analyzes their designs based on the frameworks.