This paper presents an integrated methodology for the modelling and optimisation of precedence-constrained production sequencing and scheduling for multiple production lines based on Genetic Algorithms (GA). The pro...This paper presents an integrated methodology for the modelling and optimisation of precedence-constrained production sequencing and scheduling for multiple production lines based on Genetic Algorithms (GA). The problems in this class are NP-hard combinatorial problems, requiring a triple optimisation at the same time: allocation of resources to each line, production sequencing and production scheduling within each production line. They are ubiquitous to production and manufacturing environments. Due to nature of constraints, the length of solutions for the problem can be variable. To cope with this variability, new strategies for encoding chromosomes, crossover and mutation operations have been developed. Robustness of the proposed GA is demonstrated by a complex and realistic case study.展开更多
在sm a le点估计理论引导下,利用优序列方法,研究γ-条件下,变形chebyshev迭代方法在求解Banach空间中非线性方程F(x)=0时的收敛性问题,并给出了误差估计,而且通过一个积分方程实例比较了它和N ew ton法,导数超前计值的变形N ew ton法,...在sm a le点估计理论引导下,利用优序列方法,研究γ-条件下,变形chebyshev迭代方法在求解Banach空间中非线性方程F(x)=0时的收敛性问题,并给出了误差估计,而且通过一个积分方程实例比较了它和N ew ton法,导数超前计值的变形N ew ton法,避免导数求逆的变形N ew ton法的每步误差.展开更多
文摘This paper presents an integrated methodology for the modelling and optimisation of precedence-constrained production sequencing and scheduling for multiple production lines based on Genetic Algorithms (GA). The problems in this class are NP-hard combinatorial problems, requiring a triple optimisation at the same time: allocation of resources to each line, production sequencing and production scheduling within each production line. They are ubiquitous to production and manufacturing environments. Due to nature of constraints, the length of solutions for the problem can be variable. To cope with this variability, new strategies for encoding chromosomes, crossover and mutation operations have been developed. Robustness of the proposed GA is demonstrated by a complex and realistic case study.
文摘在sm a le点估计理论引导下,利用优序列方法,研究γ-条件下,变形chebyshev迭代方法在求解Banach空间中非线性方程F(x)=0时的收敛性问题,并给出了误差估计,而且通过一个积分方程实例比较了它和N ew ton法,导数超前计值的变形N ew ton法,避免导数求逆的变形N ew ton法的每步误差.