Hybrid Particle Swarm Optimization for Stochastic Flow Shop Scheduling With No-wait Constraint
نویسندگان
چکیده
The flow shop scheduling with the no-wait constraint is a typical NP-hard combinatorial optimization problem and represents an important area in production scheduling. In this paper, a class of particle swarm optimization (PSO) approach with simulated annealing (SA) and hypothesis test (HT), namely PSOSAHT is proposed for the stochastic flow shop scheduling with no-wait constraint to minimize the maximum completion time (makespan). The developed algorithm not only applies evolutionary search guided by the mechanism of PSO, but it also applies the local search guided by the jumping mechanism of SA. Thus, both global exploration and local exploitation are balanced. Meanwhile, it applies HT to perform a statistical comparison to avoid some repeated search to some extent. Simulation results and comparisons demonstrate the feasibility, effectiveness and robustness of the proposed hybrid PSO-based algorithm.
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