An effective hybrid optimization strategy for job-shop scheduling problems
Computers and Operations Research
Designing neural networks using hybrid particle swarm optimization
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
A survey on metaheuristics for stochastic combinatorial optimization
Natural Computing: an international journal
Computers and Operations Research
Metaheuristic methods in hybrid flow shop scheduling problem
Expert Systems with Applications: An International Journal
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The stochastic flow shop scheduling with uncertain processing time is a typical NP-hard combinatorial optimization problem and represents an important area in production scheduling, which is difficult because of inaccurate objective estimation, huge search space, and multiple local minima. As a novel evolutionary technique, particle swarm optimization (PSO) has gained much attention and wide applications for both function and combinatorial problems, but there is no research on PSO for stochastic scheduling cases. In this paper, a class of PSO approach with simulated annealing (SA) and hypothesis test (HT), namely PSOSAHT is proposed for stochastic flow shop scheduling with uncertain processing time with respect to the makespan criterion (i.e. minimizing the maximum completion time). Simulation results demonstrate the feasibility, effectiveness and robustness of the proposed hybrid algorithm. Meanwhile, the effects of noise magnitude and number of evaluation on searching performances are also investigated.