为解决MTS(库存生产,Make to stock)型实木家具生产调度的问题,提高家具企业的生产效率。通过对MTS型实木家具企业生产实际特点的分析,将具有全局优化性能和强鲁棒性能特点的模拟退火算法运用到家具生产调度中,以实现实木家具生产调度...为解决MTS(库存生产,Make to stock)型实木家具生产调度的问题,提高家具企业的生产效率。通过对MTS型实木家具企业生产实际特点的分析,将具有全局优化性能和强鲁棒性能特点的模拟退火算法运用到家具生产调度中,以实现实木家具生产调度中最小化最大完工时间的优化目标。根据MTS型家具企业生产调研的数据,利用python软件进行模拟退火算法设计并进行仿真实验。结果表明,退火算法能够有效优化MTS型实木家具企业的生产调度,使生产效率提高了8.3%,这对于解决家具生产调度问题有一定的借鉴意义。展开更多
A fuzzy modeling method for complex systems is studied. The notation of general stochastic neural network (GSNN) is presented and a new modeling method is given based on the combination of the modified Takagi and Suge...A fuzzy modeling method for complex systems is studied. The notation of general stochastic neural network (GSNN) is presented and a new modeling method is given based on the combination of the modified Takagi and Sugeno's (MTS) fuzzy model and one-order GSNN. Using expectation-maximization(EM) algorithm, parameter estimation and model selection procedures are given. It avoids the shortcomings brought by other methods such as BP algorithm, when the number of parameters is large, BP algorithm is still difficult to apply directly without fine tuning and subjective tinkering. Finally, the simulated example demonstrates the effectiveness.展开更多
文摘为解决MTS(库存生产,Make to stock)型实木家具生产调度的问题,提高家具企业的生产效率。通过对MTS型实木家具企业生产实际特点的分析,将具有全局优化性能和强鲁棒性能特点的模拟退火算法运用到家具生产调度中,以实现实木家具生产调度中最小化最大完工时间的优化目标。根据MTS型家具企业生产调研的数据,利用python软件进行模拟退火算法设计并进行仿真实验。结果表明,退火算法能够有效优化MTS型实木家具企业的生产调度,使生产效率提高了8.3%,这对于解决家具生产调度问题有一定的借鉴意义。
文摘A fuzzy modeling method for complex systems is studied. The notation of general stochastic neural network (GSNN) is presented and a new modeling method is given based on the combination of the modified Takagi and Sugeno's (MTS) fuzzy model and one-order GSNN. Using expectation-maximization(EM) algorithm, parameter estimation and model selection procedures are given. It avoids the shortcomings brought by other methods such as BP algorithm, when the number of parameters is large, BP algorithm is still difficult to apply directly without fine tuning and subjective tinkering. Finally, the simulated example demonstrates the effectiveness.
