  In this paper, we propose novel mixed-integer linear programming (MIP)
formulations to model decision problems posed as influence diagrams. We also
present a novel heuristic that can be employed to warm start the MIP solver, as
well as provide heuristic solutions to more computationally challenging
problems. All of those improvements are implemented in DecisionProgramming.jl,
a new Julia package for modelling decision problems as MIP equivalents using
the proposed formulations. We provide computational results showcasing the
superior performance of these improved formulations as well as the performance
of the proposed heuristic. Lastly, we describe a novel case study showcasing
decision programming as an alternative framework for modelling multi-stage
stochastic dynamic programming problems.
