New approximate dynamic programming algorithms for large-scale undiscounted Markov decision processes and their application to optimize a production and distribution system

•We propose new approximate dynamic programming algorithms.•These algorithms can solve large-scale undiscounted Markov decision processes.•Optimal control problems for production systems with 35, 973, 840 states were solved.•The kanban, base stock, CONWIP, hybrid and extended kanban systems are cons...

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Veröffentlicht in:	European journal of operational research Jg. 249; H. 1; S. 22 - 31
Hauptverfasser:	Ohno, Katsuhisa, Boh, Toshitaka, Nakade, Koichi, Tamura, Takayoshi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier B.V 16.02.2016 Elsevier Sequoia S.A
Schlagworte:	Algorithms Approximate dynamic programming algorithms Binomial distribution Dynamic programming Iterative methods JIT-based production and distribution system Markov analysis Optimal control Optimization algorithms Production capacity Simulation Stochastic control theory Studies The curses of dimensionality Undiscounted Markov decision processes Approximate dynamic programming algorithms JIT-based production and distribution system The curses of dimensionality Optimal control Undiscounted Markov decision processes
ISSN:	0377-2217, 1872-6860
Online-Zugang:	Volltext
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