Generalized algebraic deadlock avoidance policies (DAPs) for sequential resource allocation systems (RASs) have recently been proposed as an interesting extension of the class of algebraic DAPs, that maintains the analytical representation and computational simplicity of the latter, while it guarantees completeness with respect to the maximally permissive DAP. The authors' original work that introduced these policies also provided a design methodology for them, but this methodology is limited by the fact that it necessitates the deployment of the entire state space of the considered RAS. Hence, this paper seeks the development of an alternative computational tool that can support the synthesis of correct generalized algebraic DAPs, while controlling the underlying computational complexity. More specifically, the presented correctness verification test possesses the convenient form of a mixed integer programming (MIP) formulation that employs a number of variables and constraints polynomially related to the size of the underlying RAS, and it can be readily solved through canned optimization software. Furthermore, since generalized algebraic DAPs do not admit a convenient representation in the Petri net modeling framework, an additional contribution of the presented results is that they effect the migration of the relevant past insights and developments with respect to simpler DAP classes, from the representational framework of Petri nets to that of the Deterministic Finite-State Automata.