Sequencing in an assembly line with blocking to minimize cycle time
Operations Research
A genetic algorithm for flowshop sequencing
Computers and Operations Research - Special issue on genetic algorithms
A genetic algorithm for integrating lot-sizing and sequencing in scheduling a capacitated flow line
Computers and Industrial Engineering
Scheduling flowshops with finite buffers and sequence-dependent setup times
Computers and Industrial Engineering
Flowshop scheduling with limited temporary storage
Journal of the ACM (JACM)
Swarm intelligence
An effective hybrid optimization strategy for job-shop scheduling problems
Computers and Operations Research
An effective hybrid genetic algorithm for flow shop scheduling with limited buffers
Computers and Operations Research
Meta-Lamarckian learning in memetic algorithms
IEEE Transactions on Evolutionary Computation
Classification of adaptive memetic algorithms: a comparative study
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An exact approach for batch scheduling in flexible flow lines with limited intermediate buffers
Mathematical and Computer Modelling: An International Journal
A discrete particle swarm optimization algorithm for scheduling parallel machines
Computers and Industrial Engineering
A particle swarm with selective particle regeneration for multimodal functions
WSEAS Transactions on Information Science and Applications
An efficient hybrid data clustering method based on K-harmonic means and Particle Swarm Optimization
Expert Systems with Applications: An International Journal
Adaptive Hybrid Differential Evolution Algorithm and Its Application in Fuzzy Clustering
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Minimizing the number of tardy jobs in the flowshop problem with operation and resource flexibility
Computers and Operations Research
A hybrid alternate two phases particle swarm optimization algorithm for flow shop scheduling problem
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Particle swarm optimization with selective particle regeneration for data clustering
Expert Systems with Applications: An International Journal
A hybrid harmony search algorithm for the blocking permutation flow shop scheduling problem
Computers and Industrial Engineering
Sequential metamodelling with genetic programming and particle swarms
Winter Simulation Conference
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part II
A chaotic harmony search algorithm for the flow shop scheduling problem with limited buffers
Applied Soft Computing
Expert Systems with Applications: An International Journal
A novel encoding scheme of PSO for two-machine group scheduling
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
A novel multi-objective particle swarm optimization algorithm for flow shop scheduling problems
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence
International Journal of Bio-Inspired Computation
Solving the Fm\block\Cmax problem using Bounded Dynamic Programming
Engineering Applications of Artificial Intelligence
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In this paper, an effective hybrid algorithm based on particle swarm optimization (HPSO) is proposed for permutation flow shop scheduling problem (PFSSP) with the limited buffers between consecutive machines to minimize the maximum completion time (i.e., makespan). First, a novel encoding scheme based on random key representation is developed, which converts the continuous position values of particles in PSO to job permutations. Second, an efficient population initialization based on the famous Nawaz-Enscore-Ham (NEH) heuristic is proposed to generate an initial population with certain quality and diversity. Third, a local search strategy based on the generalization of the block elimination properties, named block-based local search, is probabilistically applied to some good particles. Moreover, simulated annealing (SA) with multi-neighborhood guided by an adaptive meta-Lamarckian learning strategy is designed to prevent the premature convergence and concentrate computing effort on promising solutions. Simulation results and comparisons demonstrate the effectiveness of the proposed HPSO. Furthermore, the effects of some parameters are discussed.