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A Quantum Genetic Based Scheduling Algorithm for stochastic flow shop scheduling problem with random breakdown

Abstract A Quantum Genetic Based Scheduling Algorithm (QGBSA) for stochastic flow shop scheduling with random breakdown and random repair time is proposed in this paper, which combines stochastic programming and stochastic simulation theory, quantum compute and genetic algorithm together. In the QGBSA, the Q-bit based representation in discrete 0-1 hyperspace is employed, which is then converted into decimal scheduling code, and quantum gate is used to update the current generation. Meanwhile, catastrophe operator is added to avoid premature. In order to improve the effectiveness of scheduling scheme, the stochastic programming theory is used to set up a stochastic flow shop scheduling model without breakdown. Then we consider two different working modes—preemptive-resume and preemptive-repeat under breakdown. The former processes the rest part of interrupted job while the later reprocesses the interrupted job. Under each working mode, the situations where breakdown happened at any time or at machine's life-span time were discussed. Finally, compared with traditional Genetic Algorithm (GA), computational results show the feasibility and effectiveness of QGBSA policy.