单件生产系统同步加工动态调度策略

Dynamic Scheduling Policy for Synchronized Processing in One-of-a-kind Production Systems

研究以最小化同步加工时间差和最小化完工时间为目标的单件生产系统动态调度问题,并提出一种基于耦合瞬态混沌神经网络方法的滚动时域动态调度策略。新的能量函数表达式能同时包含优化目标和工艺路线及资源等约束;针对能量函数中包含实值和0-1值两种变量的特点,提出将神经网络分成两个相互耦合的子网,分别处理两种类型的神经元;在网络动态方程中加入自抑制反馈混沌特性来提高获得全局最优解的能力。应用滚动时域分解法获得动态调度策略,以适应加工过程中不断变化的同步程度。仿真试验结果表明,单件生产系统动态同步加工调度策略具有良好的全局优化性能和运算效率。

Aiming at minimizing the time difference in synchronized processing of various components of a product and the makespan of all the jobs,a rolling horizon dynamic scheduling policy based on coupled transient chaotic neural network is investigated for a one-of-a-kind production system.A novel energy function expression,which includes the two objectives,the constraints of operation precedence and resource sharing,is constructed.Because there are two types of variables（i.e.,the real type and the Boolean type） in the energy function,the transient chaotic neural network is decomposed into two coupled subnets to handle the two types of neurons respectively.A negative self-feedback chaotic item is included in the dynamic equation to improve the ability of searching the global optimum.A dynamic scheduling policy based on rolling horizon decomposition is proposed to adapt to the continuous changing of synchronization level during the processing.Simulation results indicate that the proposed algorithm has good global optimization capability and can improve the computational efficiency significantly.