A novel discrete quantum-behaved particle swarm optimization (DQPSO) approach was proposed to address Job-shop scheduling (JSP) problem. JSP is a complex combinatorial optimization problem with many variations, and it is strong nondeterministic polynomial time (NP)-complete. The proposed DQPSO approach utilized the principle of quantum-PSO and described the particle positions with quantum wave function. Crossover and mutation operators in GA were involved which makes DQPSO applicable for searching in combinatorial space directly.In addition, a new two-layer local searching algorithm was also incorporated into the DQPSO algorithm. The two-layer local searching algorithm randomly generated new particles around the local optimums, which in turn updated solutions with high quality and diversity.The application demonstrated that DQPSO can achieve better results on most benchmark scheduling problems．
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