期刊
OPERATIONS RESEARCH
卷 51, 期 6, 页码 850-865出版社
INFORMS
DOI: 10.1287/opre.51.6.850.24925
关键词
dynamic programming/optimal control : approximations/large-scale problems; queues, algorithms : control of queueing networks
The curse of dimensionality gives rise to prohibitive computational requirements that render infeasible the exact solution of large-scale stochastic control problems. We study an efficient method based on linear programming for approximating solutions to such problems. The approach fits a linear combination of pre-selected basis functions to the dynamic programming cost-to-go function. We develop error bounds that offer performance guarantees and also guide the selection of both basis functions and state-relevance weights that influence quality of the approximation. Experimental results in the domain of queueing network control provide empirical support for the methodology.
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