In the domain of decision theoretic planning, the factored framework (FMDP) has produced optimized algorithms using Decision Trees (SVI, SPI) and Algebraic Decision Diagrams (SPUDD). However, the state-of-the-art SPUDD algorithm requires i) the problem to be specified with binary variables and ii) the data structures to share a common order on variables. In this article, we propose a new algorithm within the factored framework that eliminates both these requirements. We compare our approach to the SPUDD algorithm. Experimental results show that our algorithm allows significant gains in time, illustrating a better trade-off between theoretical complexity of algorithms and size of representation.