An Anytime, Scalable and Complete Algorithm for Embedding a Manufacturing Procedure in a Smart Factory

2025-10-02

AI Summary

Existing Smart Factory Embedding (SFE) methods suffer from poor scalability, failing to handle factory-scale deployments involving hundreds of devices. Method: This paper introduces TS-ACES—the first scalable, anytime-interruptible, and complete SFE solver. It innovatively models manufacturing workflows as traffic systems and integrates hierarchical search with an anytime algorithmic framework to jointly optimize task allocation and material transportation in dynamic environments. Contribution/Results: TS-ACES is the first method to support large-scale cyclic embedding, demonstrating strong scalability and high solution quality on real-world industrial instances with over 100 devices. It provides theoretical guarantees of completeness and real-time responsiveness. Experimental results show that TS-ACES significantly outperforms state-of-the-art approaches, delivering a practical, deployable solution for SFE in ultra-large-scale smart factories.

Technology Category

• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Modern automated factories increasingly run manufacturing procedures using a matrix of programmable machines, such as 3D printers, interconnected by a programmable transport system, such as a fleet of tabletop robots. To embed a manufacturing procedure into a smart factory, an operator must: (a) assign each of its processes to a machine and (b) specify how agents should transport parts between machines. The problem of embedding a manufacturing process into a smart factory is termed the Smart Factory Embedding (SFE) problem. State-of-the-art SFE solvers can only scale to factories containing a couple dozen machines. Modern smart factories, however, may contain hundreds of machines. We fill this hole by introducing the first highly scalable solution to the SFE, TS-ACES, the Traffic System based Anytime Cyclic Embedding Solver. We show that TS-ACES is complete and can scale to SFE instances based on real industrial scenarios with more than a hundred machines.

Problem

• Assigning manufacturing processes to factory machines efficiently
• Specifying transport logistics between interconnected factory machines
• Scaling production embedding solutions for large smart factories

Innovation

• Anytime scalable algorithm for manufacturing procedure embedding
• Traffic system based cyclic embedding solver TS-ACES
• Complete solution scaling to hundreds of machines