A tutorial survey of job-shop scheduling problems using genetic algorithms—I: representation
Computers and Industrial Engineering
Genetic algorithms for flowshop scheduling problems
Computers and Industrial Engineering
Computers and Industrial Engineering - Special issue on computational intelligence for industrial engineering
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Two-phase sub population genetic algorithm for parallel machine-scheduling problem
Expert Systems with Applications: An International Journal
Heuristic algorithms for two machine re-entrant flow shop
Theoretical Computer Science
A hybrid genetic algorithm that optimizes capacitated vehicle routing problems
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Proceedings of the 40th Conference on Winter Simulation
A single-machine bi-criterion learning scheduling problem with release times
Expert Systems with Applications: An International Journal
Simulation-based two-phase genetic algorithm for the capacitated re-entrant line scheduling problem
Computers and Industrial Engineering
A decision support approach for assigning reviewers to proposals
Expert Systems with Applications: An International Journal
Computers and Operations Research
Solving flexible flow-shop problem with a hybrid genetic algorithm and data mining: A fuzzy approach
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Minimizing total completion time for re-entrant flow shop scheduling problems
Theoretical Computer Science
Solving a two-agent single-machine scheduling problem considering learning effect
Computers and Operations Research
Assembly line balancing in garment industry
Expert Systems with Applications: An International Journal
A study of the single-machine two-agent scheduling problem with release times
Applied Soft Computing
Collaboration and Competition Process: A Multi-Teams and Genetic Algorithm Hybrid Approach
International Journal of Artificial Life Research
Engineering Applications of Artificial Intelligence
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories
Hi-index | 12.06 |
This study considers the production environment of the re-entrant flow-shop (RFS). In a RFS, all jobs have the same routing over the machines of the shop and the same sequence is traversed several times to complete the jobs. The aim of this study is to minimize makespan by using the genetic algorithm (GA) to move from local optimal solution to near optimal solution for RFS scheduling problems. In addition, hybrid genetic algorithms (HGA) are proposed to enhance the performance of pure GA. The HGA is compared to the optimal solutions generated by the integer programming technique, and to the near optimal solutions generated by pure GA and the non-delay schedule generation procedure. Computational experiments are performed to illustrate the effectiveness and efficiency of the proposed HGA algorithm.