  There are no computationally feasible algorithms that provide solutions to
the finite horizon Risk-sensitive Constrained Markov Decision Process
(Risk-CMDP) problem, even for problems with moderate horizon. With an aim to
design the same, we derive a fixed-point equation such that the optimal policy
of Risk-CMDP is also a solution. We further provide two optimization problems
equivalent to the Risk-CMDP. These formulations are instrumental in designing a
global algorithm that converges to the optimal policy. The proposed algorithm
is based on random restarts and a local improvement step, where the local
improvement step utilizes the solution of the derived fixed-point equation;
random restarts ensure global optimization. We also provide numerical examples
to illustrate the feasibility of our algorithm for inventory control problem
with risk-sensitive cost and constraint. The complexity of the algorithm grows
only linearly with the time-horizon.
