American Institute of Mathematical Sciences

Advanced
July  2007, 3(3): 429-444. doi: 10.3934/jimo.2007.3.429

Learning algorithms for finite horizon constrained Markov decision processes

A. Mittal 1, and  N. Hemachandra 1,

1. 

IE and OR Interdisciplinary Programme, IIT Bombay, Mumbai, 400 076, India, India

Received  April 2006 Revised  November 2006 Published  July 2007

We propose a heuristic and two stochastic approximation based learning algorithms for finite horizon, finite state-action constrained Markov decision models. We include models and numerical examples arising from risk management in fund allocation, retailer-depot product availability in a supply chain and admission control in a simple queue, that have to satisfy performance based constraints.
Keywords: Markov Decision Processes, Risk management, Supply chains, Control of queues..
Mathematics Subject Classification: Primary: 90B50; Secondary: 11K45, 90C4.
Citation: A. Mittal, N. Hemachandra. Learning algorithms for finite horizon constrained Markov decision processes. Journal of Industrial & Management Optimization, 2007, 3 (3) : 429-444. doi: 10.3934/jimo.2007.3.429
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