Simulation-based Ordinal Optimization Methods with Applications to Production Scheduling(2/3)
Date Issued
2004-07-31
Date
2004-07-31
Author(s)
DOI
922212E002060
Abstract
The second year of research includes three
tasks (1) development of fast simulation for solving
the Markov decision process formulation of
dispatching problem by combining ordinal
optimization and policy iteration, (2) investigation of
applicability of re-enforcement learning to Markov
decision process formulation of dispatching problems,
and (3) niche assessment by literature survey of
logistic information application service provider for
electronics industry.
Subjects
派工
馬可夫決策過程
增強式學習
時
變
變
全球運籌資訊服務體系
資訊服務網路
Publisher
臺北市：國立臺灣大學電機工程學系暨研究所
Type
report
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