In this paper some aspects are treated of the implementation of Markov decision models. As illustration a cash control problem of a bank is used. It is emphasized that optimality is not decisive when choosing a strategy : by the optimization procedure one may find good strategies and these help to construct and evaluate other strategies. Also the organizational aspect of implementation (centralized or decentralized decision making ) is discussed. It is demonstrated that, even without formal implementation, the construction of a Markov decision model can be very useful
Problems of sequential decisions are marked by the fact that the consequences of a decision made at ...
Markov decision processes are a flexible technique for stochastic and dynamic optimization problems...
In this paper we review how models for discrete random systems may be used to support practical deci...
The paper is concerned with applicational aspects of discrete random systems. Such systems appear in...
It is over 30 years ago since D.J. White started his series of surveys on practical applications of ...
A Markov decision process (MDP) relies on the notions of state, describing the current situation of ...
A short tutorial introduction is given to Markov decision processes (MDP), including the latest acti...
Markov Decision Problems (MDPs) are the foundation for many problems that are of interest to researc...
The issues concerning the potential application of Markov processes theory for efficient management ...
We provide a tutorial on the construction and evalua-tion of Markov decision processes (MDPs), which...
Markov Decision Processes (MDPs) are a mathematical framework for modeling sequential decision probl...
An absorbing Markov chain is introduced in order to give a mathematical formulation of the decision ...
The main topic of the paper is the relation between modelling and numerical analysis for Markov deci...
Markov decision processes provide a rigorous mathematical framework for sequential decision making u...
Markov decision problems (MDPs) provide the foundations for a number of problems of interest to AI r...
Problems of sequential decisions are marked by the fact that the consequences of a decision made at ...
Markov decision processes are a flexible technique for stochastic and dynamic optimization problems...
In this paper we review how models for discrete random systems may be used to support practical deci...
The paper is concerned with applicational aspects of discrete random systems. Such systems appear in...
It is over 30 years ago since D.J. White started his series of surveys on practical applications of ...
A Markov decision process (MDP) relies on the notions of state, describing the current situation of ...
A short tutorial introduction is given to Markov decision processes (MDP), including the latest acti...
Markov Decision Problems (MDPs) are the foundation for many problems that are of interest to researc...
The issues concerning the potential application of Markov processes theory for efficient management ...
We provide a tutorial on the construction and evalua-tion of Markov decision processes (MDPs), which...
Markov Decision Processes (MDPs) are a mathematical framework for modeling sequential decision probl...
An absorbing Markov chain is introduced in order to give a mathematical formulation of the decision ...
The main topic of the paper is the relation between modelling and numerical analysis for Markov deci...
Markov decision processes provide a rigorous mathematical framework for sequential decision making u...
Markov decision problems (MDPs) provide the foundations for a number of problems of interest to AI r...
Problems of sequential decisions are marked by the fact that the consequences of a decision made at ...
Markov decision processes are a flexible technique for stochastic and dynamic optimization problems...
In this paper we review how models for discrete random systems may be used to support practical deci...