Improved iterated greedy algorithm for reentrant flow shop scheduling problem
The reentrant hybrid flow shop adds the reentrant feature to the hybrid flow shop and has a higher schedu-ling complexity.To solve the reentrant hybrid flow shop scheduling problem,a scheduling optimization model was established with the objective of minimizing the maximum completion time,and then a Learning Iterated Greedy al-gorithm with Elite Adjustment(LIG-EA)was proposed.The LIG-EA algorithm used job-based encoding,and then decoded the reconstituted chromosomes.The population was divided into two parts,elite individuals and ordinary individuals,and elite destruction with reconstruction and chromosome adjustment based on key jobs were carried out for elite individuals,and the construction of learning mechanisms and destruction with reconstruction for ordinary individuals.To improve the initial population quality,the NEH heuristic algorithm was used for population initial-ization,and the insertion validity judgment was added to the reconstruction operation for the re-entry characteristics of the reentrant hybrid flow shop to improve the speed of the algorithm.Through extensive experiments,the results showed that the LIG-EA algorithm could effectively solve the reentrant hybrid flow shop scheduling problem.
reentrant hybrid flow shop schedulingiterated greedy algorithmelite solution set constructionkey job adjustmentlearning method construction
万方数据
维普
