A Survey on Parallel Genetic Algorithms for Shop Scheduling Problems

@article{Luo2018ASO,
  title={A Survey on Parallel Genetic Algorithms for Shop Scheduling Problems},
  author={Jia-xiang Luo and Didier El Baz},
  journal={2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)},
  year={2018},
  pages={629-636}
}
  • Jia-xiang Luo, D. E. Baz
  • Published 1 May 2018
  • Computer Science
  • 2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)
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… Expand
Parallel Metaheuristics for Shop Scheduling: enabling Industry 4.0
TLDR
The results support that parallel metaheuristic have potential to tackle Industry 4.0 scheduling problems, but it is essential to extend the research to the cloud and edge computing, flexible shop configurations, dynamic problems with multi-resource, and multi-objective optimization. Expand
Highly scalable parallel genetic algorithm on Sunway many-core processors
TLDR
A highly scalable hybrid parallel genetic algorithm (HPGA) based on Sunway TaihuLight Supercomputer that can fully exploit the individual diversity of the genetic algorithm and reasonably maintain the communication overhead is proposed. Expand
Experimental Comparison between Genetic Algorithm and Ant Colony Optimization on Traveling Salesman Problem
TLDR
The Traveling Salesman Problem (TSP) is solved using Swarm Intelligence algorithms and the GA and ACO algorithms are compared and it is shown that ACO was much easier to control. Expand
EVO* 2019 - Late-Breaking Abstracts Volume
This volume contains the Late-Breaking Abstracts submitted to the EVO* 2019 Conference, that took place in Leipzig, from 24 to 26 of April. These papers where presented as short talks and also at theExpand
A parallel Architecture of a Genetic Algorithm for EIT Image Reconstruction
TLDR
The focus will be on the image reconstruction algorithm in order to reach high-quality images of electrical impedance tomography, where the genetic algorithm was able to outperform commonly used algorithms in terms of fitness values. Expand
Evolutionary optimization of contexts for phonetic correction in speech recognition systems
TLDR
The results show the viability of a genetic algorithm as a tool for context optimization, which, added to a post-processing correction based on phonetic representations, can reduce the errors on the recognized speech. Expand
Optimización evolutiva de contextos para la corrección fonética en sistemas de reconocimiento del habla
TLDR
The results show the viability of a genetic algorithm as a tool for context optimization, which added to a post-processing correction based on phonetic representations is able to reduce the errors on the recognized speech. Expand
A Multi-strategy LSHADE Algorithm and its Applications on Temporal Alignment
TLDR
Novel way to evaluate algorithmically created boxes using Sharpe Ratio Concept from finance is demonstrated and this paper discovers novel products based on the distance threshold from the optimal solutions and adapted way to evaluates boxes with Sharpe Scores. Expand

References

SHOWING 1-10 OF 60 REFERENCES
Investigating Parallel Genetic Algorithms on Job Shop Scheduling Problems
TLDR
Using the Giffler and Thompson algorithm, two new operators are created, THX crossover and mutation, which better transmit temporal relationships in the schedule, and appear to integrate successfully the advantages of coarse-grain and fine-grain GAs. Expand
A parallel genetic algorithm for the job shop scheduling problem
TLDR
This paper presents a parallel genetic algorithm for the job shop scheduling problem (JSP) that is preeminent in comparison with other methods on both the calculation time and the speed of finding optimal solutions. Expand
Solving flow shop scheduling problem using a parallel genetic algorithm
TLDR
According to the experimental results, the proposed parallel genetic algorithm (PPGA) considerably decreases the CPU time without adversely affecting the makespan. Expand
A new hybrid parallel genetic algorithm for the job-shop scheduling problem
TLDR
This paper proposes a new hybrid parallel genetic algorithm with specialized crossover and mutation operators utilizing path-relinking concepts from combinatorial optimization approaches and tabu search in particular and compares fairly well with some of the best-performing genetic algorithms for the JSSP. Expand
Parallel Genetic Algorithm for the Flow Shop Scheduling Problem
The permutation flow shop sequencing problem with the objective of minimizing the sum of the job’s completion times, in literature known as the F||C sum , has been considered. The parallel geneticExpand
Parallel Genetic Algorithm for solving Job-Shop Scheduling Problem Using Topological sort
  • Archit Somani, D. Singh
  • Computer Science
  • 2014 International Conference on Advances in Engineering & Technology Research (ICAETR - 2014)
  • 2014
TLDR
This paper presents Parallel Genetic Algorithm (PGA) by Using Topological sorting, which is able to improve the solution of JSSP, and minimizes the execution time for Make span calculation by using PGA. Expand
A tutorial survey of job-shop scheduling problems using genetic algorithms—I: representation
TLDR
A tutorial survey of recent works on solving classical JSP using genetic algorithms using various hybrid approaches of genetic algorithms and conventional heuristics is given. Expand
Parallel Genetic Algorithm for Minimizing Total Weighted Completion Time
TLDR
This work proposes a very effective parallel genetic algorithm PGA and methods of determining lower and upper bounds of the objective function and adds random generated unfeasible solutions to the population. Expand
An agent-based parallel approach for the job shop scheduling problem with genetic algorithms
TLDR
An agent-based parallel approach for the job shop scheduling problem in which creating the initial population and parallelizing the genetic algorithm are carried out in an agent- based manner is proposed and shows that this approach improves the efficiency. Expand
A hybrid genetic algorithm for the job shop scheduling problem
TLDR
This paper presents a hybrid genetic algorithm for the job shop scheduling problem that is based on random keys and tested on a set of standard instances taken from the literature and compared with other approaches. Expand
...
1
2
3
4
5
...