基于混合QPSO的模糊柔性作业车间调度问题研究

针对加工时间不确定的柔性作业车间调度问题,利用三角模糊数表征相关的时间参数并提出了一种混合量子粒子群算法进行求解,在充分发挥量子粒子群算法全局搜索能力的基础上,设计了边界修正与协作更新策略增加其搜索效率,同时使用交叉算子和路径重连技术直接对优秀粒子所映射的工序编码操作,弥补了大多数连续算法在求解离散问题时深度发掘能力不足的弊端。通过5个经典的测试算例以及光纤制造车间的实例分析,说明所提方法在寻优效率上要高于原始的量子粒子群算法和另外3种近期文献中的算法,具有较好的实际运用价值。

Research on Fuzzy Flexible Job Shop Scheduling Problem Based on Hybrid QPSO

To solve the flexible job shop scheduling problem of uncertain processing time, triangular fuzzy numbers are used to characterize the relevant time parameters and a Hybrid Quantum Particle Swarm Optimization (HQPSO) is proposed. On the basis of making full use of the global search capability of Quantum Particle Swarm Optimization, the search efficiency is increased by designing a boundary repair strategy and a cooperative update strategy. Meanwhile, the cross-operator and path relinking technique are used to in the operation sequence mapped by the excellent particles, which makes up the disadvantages of the insufficient ability of deeply exploration of the most continuous algorithms when solving discrete problems. Five classic test examples and an instance of an optical fiber manufacturing workshop are analyzed and show that the proposed method is better than the original QPSO and three other algorithms mentioned in recent literature and has good practical value.