A study of permutation crossover operators on the traveling salesman problem
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic algorithms for flowshop scheduling problems
Computers and Industrial Engineering
A heuristic algorithm for mean flowtime objective in flowshop scheduling
Computers and Operations Research
AllelesLociand the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
Comparison of heuristics for flowtime minimisation in permutation flowshops
Computers and Operations Research
A combinatorial particle swarm optimisation for solving permutation flowshop problems
Computers and Industrial Engineering
Computers and Operations Research
Applying adaptive algorithms to epistatic domains
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
Two ant-colony algorithms for minimizing total flowtime in permutation flowshops
Computers and Industrial Engineering - Special issue: Selected papers from the 30th international conference on computers; industrial engineering
New VNS heuristic for total flowtime flowshop scheduling problem
Expert Systems with Applications: An International Journal
Hybridizing VNS and path-relinking on a particle swarm framework to minimize total flowtime
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
In this study, the permutation flowshop scheduling problem with the total flowtime criterion is considered. An asynchronous genetic local search algorithm (AGA) is proposed to deal with this problem. The AGA consists of three phases. In the first phase, an individual in the initial population is yielded by an effective constructive heuristic and the others are randomly generated, while in the second phase all pairs of individuals perform the asynchronous evolution (AE) where an enhanced variable neighborhood search (E-VNS) as well as a simple crossover operator is used. A restart mechanism is applied in the last phase. Our experimental results show that the algorithm proposed outperforms several state-of-the-art methods and two recently proposed meta-heuristics in both solution quality and computation time. Moreover, for 120 benchmark instances, AGA obtains 118 best solutions reported in the literature and 83 of which are newly improved.