Jointly Assigning Processes to Machines and Generating Plans for Autonomous Mobile Robots in a Smart Factory

TL;DR

This paper introduces ACES, a novel solver that jointly optimizes process-to-machine assignments and mobile robot paths in smart factories, significantly improving throughput over traditional sequential methods.

Contribution

The paper presents ACES, the first solver to simultaneously optimize process assignment and agent routing in smart factories, enabling better scalability and efficiency.

Findings

ACES scales to real industrial scenarios

Joint optimization improves factory throughput

Outperforms sequential management systems

Abstract

A modern smart factory runs a manufacturing procedure using a collection of programmable machines. Typically, materials are ferried between these machines using a team of mobile robots. To embed a manufacturing procedure in a smart factory, a factory operator must a) assign its processes to the smart factory's machines and b) determine how agents should carry materials between machines. A good embedding maximizes the smart factory's throughput; the rate at which it outputs products. Existing smart factory management systems solve the aforementioned problems sequentially, limiting the throughput that they can achieve. In this paper we introduce ACES, the Anytime Cyclic Embedding Solver, the first solver which jointly optimizes the assignment of processes to machines and the assignment of paths to agents. We evaluate ACES and show that it can scale to real industrial scenarios.