Hybrid particle swarm optimization for stochastic flow shop scheduling with no-wait constraint

The stochastic flow shop scheduling problem with no-wait constraint, which requires jobs have to be processed without interruption between consecutive machines and with uncertain processing time, is a typical NP-hard combinatorial optimization problem and represents an important area in production scheduling. And it is also difficult because of inaccurate objective estimation, huge search space, and multiple local minima. As a novel evolutionary technique, particle swarm optimization (PSO) has attracted much attention and wide applications for both function and combinatorial problems, but there is no research on PSO for stochastic flow shop scheduling with no-wait constraint. In this paper, a class of PSO approach with simulated annealing (SA) and hypothesis test (HT), namely PSOSAHT is proposed for the above problem with respect to the makespan criterion (i.e. minimizing the maximum completion time). Simulation results and comparisons based on well-known benchmarks 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.