Application of orthogonal arrays and MARS to inventory forecasting stochastic dynamic programs

被引:22
作者
Chen, VCP [1 ]
机构
[1] Georgia Inst Technol, Sch Ind & Syst Engn, Atlanta, GA 30332 USA
关键词
dynamic programming; orthogonal arrays; regression splines;
D O I
10.1016/S0167-9473(98)00084-X
中图分类号
TP39 [计算机的应用];
学科分类号
081203 [计算机应用技术]; 0835 [软件工程];
摘要
This paper describes the solution to inventory forecasting problems using a statistical perspective of the high-dimensional continuous-state stochastic dynamic programming (SDP) optimization model. In particular, the accuracy of the OA/MARS SDP solution method (Chen et al., 1999, Oper. Res., to appear), which employs orthogonal arrays and multivariate adaptive regression splines, is examined via simulations which vary certain user-specified parameters. For continuous-state SDP, the current definition of high-dimensional is more than five state variables. Most continuous-state problems require an approximate solution through discretization of the state space and estimation of the future value function. Under a statistical perspective, the discretization and the future value function are analogous to an experimental design and an unknown mean response. (C) 1999 Elsevier Science B.V. All rights reserved.
引用
收藏
页码:317 / 341
页数:25
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