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研究以最小化完工时间为目标的模糊加工时间零等待多产品厂间歇调度问题,提出一种基于差分进化粒子群优化(DEPSO)的间歇调度算法.以基本粒子群算法为整体进化框架,采用基于反向学习的方法初始化种群,引入群体极值保持代数作为阈值,利用基于排序的差分进化算法优化粒子个体极值位置,改变粒子的搜索范围,防止粒子陷入局部极值.仿真实验验证了所提算法在解决模糊加工时间零等待多产品厂间歇调度问题上的有效性和优越性.
A batch scheduling algorithm based on differential evolution particle swarm optimization (DEPSO) is proposed to solve the problem of zero waiting for the multi-product factory intermittent scheduling with the aim of minimizing the completion time. Aiming at the evolutionary framework of the whole particle swarm optimization algorithm, Inverse learning method is used to initialize the population, population algebra is used to maintain the algebra as the threshold, and the ranking evolutionary algorithm is used to optimize the extremum location of particles and change the search range of particle to prevent the particles from falling into local extremum. The algorithm is effective and superior in solving the problem of zero wait for multi-product plant intermittent scheduling problem with fuzzy processing time.