Self-optimization in distributed manufacturing systems using Modular State-based Stackelberg Games

TL;DR

This paper introduces Modular State-based Stackelberg Games (Mod-SbSG), a hierarchical decision-making framework for distributed manufacturing systems that improves cooperation, reduces overflow, and decreases power consumption through self-learning agents.

Contribution

The paper develops a novel hierarchical game structure combining State-based Potential Games with Stackelberg games for distributed manufacturing.

Findings

Reduces overflow by 97.1% compared to vanilla SbPG

Decreases power consumption by 5-13% while meeting demand

Provides convergence guarantees for the hierarchical game structure

Abstract

In this study, we introduce Modular State-based Stackelberg Games (Mod-SbSG), a novel game structure developed for distributed self-learning in modular manufacturing systems. Mod-SbSG enhances cooperative decision-making among self-learning agents within production systems by integrating State-based Potential Games (SbPG) with Stackelberg games. This hierarchical structure assigns more important modules of the manufacturing system a first-mover advantage, while less important modules respond optimally to the leaders' decisions. This decision-making process differs from typical multi-agent learning algorithms in manufacturing systems, where decisions are made simultaneously. We provide convergence guarantees for the novel game structure and design learning algorithms to account for the hierarchical game structure. We further analyse the effects of single-leader/multiple-follower and multiple-leader/multiple-follower scenarios within a Mod-SbSG. To assess its effectiveness, we implement and test Mod-SbSG in an industrial control setting using two laboratory-scale testbeds featuring sequential and serial-parallel processes. The proposed approach delivers promising results compared to the vanilla SbPG, which reduces overflow by 97.1%, and in some cases, prevents overflow entirely. Additionally, it decreases power consumption by 5-13% while satisfying the production demand, which significantly improves potential (global objective) values.