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 considered. We show numerical comparisons between optimal controls and optimized pull controls.
approximate dynamic programming algorithms undiscounted markov decision processes optimal control the curses of dimensionality jit-based production and distribution system