Hierarchical routing control in discrete manufacturing plants via model predictive path allocation and greedy path following

TL;DR

This paper introduces a hierarchical control method for real-time routing in discrete manufacturing plants, combining model predictive path allocation with greedy path following, demonstrating high efficiency and effectiveness in simulations.

Contribution

It presents a novel hierarchical approach using a Lagrangian model and predictive control for routing, improving upon previous Eulerian-based methods.

Findings

High performance in simulations with large prediction horizons

Efficient computational times for complex routing tasks

Potential for experimental validation on a pilot plant

Abstract

The problem of real-time control and optimization of components' routing in discrete manufacturing plants, where distinct items must undergo a sequence of jobs, is considered. This problem features a large number of discrete control inputs and the presence of temporal-logic constraints. A new approach is proposed, adopting a shift of perspective with respect to previous contributions, from a Eulerian system model that tracks the state of plant nodes, to a Lagrangian model that tracks the state of each part being processed. The approach features a hierarchical structure. At a higher level, a predictive receding horizon strategy allocates a path across the plant to each part in order to minimize a chosen cost criterion. At a lower level, a path following logic computes the control inputs in order to follow the assigned path, while satisfying all constraints. The approach is tested here in simulations, reporting extremely good performance as measured by closed-loop cost function values and computational efficiency, also with very large prediction horizon values. These features pave the way to a number of subsequent research steps, which will culminate with the experimental testing on a pilot plant.