A genetic algorithm for flowshop sequencing
Computers and Operations Research - Special issue on genetic algorithms
A real-world scheduling problem using genetic algorithms
ICC&IE '94 Proceedings of the 17th international conference on Computers and industrial engineering
Journal of Parallel and Distributed Computing - Special issue on parallel evolutionary computing
Efficient scheduling of arbitrary task graphs to multiprocessors using a parallel genetic algorithm
Journal of Parallel and Distributed Computing - Special issue on parallel evolutionary computing
An evolutionary approach to multiprocessor scheduling of dependent tasks
Future Generation Computer Systems - Special issue: Bio-inspired solutions to parallel processing problems
Tabu search for total tardiness minimization in flowshop scheduling problems
Computers and Operations Research
Computers and Industrial Engineering
Simulated annealing heuristic for flow shop scheduling problems with unrelated parallel machines
Computers and Operations Research
Journal of Parallel and Distributed Computing
Modeling realistic hybrid flexible flowshop scheduling problems
Computers and Operations Research
Evolutionary computing in manufacturing industry: an overview of recent applications
Applied Soft Computing
Integrating simulation and optimization to schedule a hybrid flow shop with maintenance constraints
Computers and Industrial Engineering
NP-complete scheduling problems
Journal of Computer and System Sciences
Computers and Industrial Engineering
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This paper introduces some advanced genetic algorithms for a complex hybrid flexible flow line problem with a makespan objective that was recently formulated. General precedence constraints among jobs are taken into account, as are machine release dates, time lags and sequence dependent set-up times; both anticipatory and non-anticipatory. This combination of constraints captures many real world industrial problems; among those is the ceramic tile production that served as our inspiration. The introduced algorithms employ solution representation schemes with different degrees of directness. Several new machine assignment rules are introduced and implemented in some proposed genetic algorithms. The different genetic algorithms are compared among each other and to some heuristics as well. The results indicate that simple solution representation schemes result in the best performance, even for complex scheduling problems and that the genetic algorithms lead to a better solution quality than all tested heuristics.