- 2016 Elsevier B.V. In multi-parametric programming, an optimization problem is solved as a function of certain bounded parameters. Hence it requires the exploration of the corresponding parameter space, a procedure which inherently leads to independent subproblems to be solved for each part of the parameter space. This characteristic is used to develop a parallelization strategy for many classes of multi-parametric programming algorithms. The trade-off between information overhead and independence of each machine is addressed explicitly through the introduction of a user-defined parameter. This novel approach is applied to a geometrical multi-parametric quadratic programming algorithm; a computational study as well as the application to a combined heat and power heat recovery subsystem show the benefits of the developed approach.