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

✍ Scribed by Victoria C.P Chen

• Publisher: Elsevier Science
• Year: 1999
• Tongue: English
• Weight: 374 KB
• Volume: 30
• Category: Article
• ISSN: 0167-9473
• DOI: 10.1016/s0167-9473(98)00084-x

✦ Synopsis

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-speciÿed parameters. For continuous-state SDP, the current deÿnition of high-dimensional is more than ÿve 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.