RRPDG: A Graph Model to Enable AI-Based Production Reconfiguration and Optimization

Abstract

This article introduces the regionalized resource process dependence graphs (RRPDGs): a manufacturing processes representation inspired by the regionalized value state dependence graphs traditionally used in software compilers. An RRPDG is an ordered sequence of nodes, each characterized by stereotyped input and output parameters, encapsulating a transformation of the process state (e.g., a manufacturing operation). RRPDG allow defining complex transformations by composing a set of nodes (i.e., regions), hiding the internal details. Then, RRPDGs are used to automatically reasoning over dynamic reconfiguration and process optimization: an instance of the Astar search algorithm is used to search for possible transformations while pursuing an optimization function. The rules defined in this article over RRPDG models enforce the transformations' correctness. We use RRPDGs to model a real production system while the transformation rules are applied to optimize the system's processes. The proposed representation reduced the search complexity in each experiment, allowing to reach an optimal solution also in the case for which classical approaches were unable to complete before reaching the timeout. In all the experiments, the cost of the solution produced by using the regionalized representation is minor than the the solution produced by using the classical representation.