串行生产线的参数优化

PARAMETER OPTIMIZATION FOR PRODUCTION SYSTEMS IN SERIES

对随机离散事件系统模型，用实验（或模拟）方法进行扰动分析（ｐｅｒｔｕｒｂａｔｉｏｎＡｎａｌｙｓｉｓ，简称ＰＡ），对固定的样本，得到性能指标（设为Ｊ（θ））对可调参数θ的梯度的估计．用固定长度的观测值（如Ｌ个顾客）估计，将估计值代入随机逼近算法，递推地求最优参数，得到了基于扰动分析的优化算法．实验结果表明，这种优化算法，有较好的收敛速度．对串行生产线，提出每离开Ｌ个顾客递推一次参数的优化算法，并证明了这种算法可收敛到使Ｊ（θ）达极小的θ．

Based on a fixed sample path, perturbation analysis (PA) offers an estimate for the gradient - dJ(θ)/dθ of performance measure J(θ) with respect to the adjustable parameter θ for stochastic discrete event systems. The PA estimate of dJ(θ)/dθ using fixed length of observation (e. g., L customers) is then put into the stochastic approximation algorithm which recursively optimize the parameter. This is the socalled 'Single-Run-Optimization' algorithm. Experiment results show that this kind of algorithms has relatively fast convergence rate. For production systems in series this paper proposes an optimization algorithm which iterates once after every L customers' departure and proves that the algorithm converges to θ which minimizes J(θ).