The self-consistent parametric inference algorithm: Optimisation in continuous search space

The self-consistent parametric inference algorithm represents a dynamical optimisation procedure in continuous search space based on a parametrised approach and supposed to solve procedural tasks in operations research. Continuous parametric descriptions and estimation by inference are extended to a functional self-consistency approach, which in the present work we discuss in the context of typical problems for flexible manufacturing systems. We focus on the question, once the problem is formally parametrised, whether the hypothesis yields a reasonable solution in a continuous search space. The constrained search space profile becomes visible through an analytical approximate solution and can be analysed in the spirit of meta-heuristics. Additionally, a quality measure for the solution is given, which extends the solving algorithm to a learning procedure yielding a guide for the progressive production of an adequate solution to the given optimisation problem.