Jointly Assigning Processes to Machines and Generating Plans for Autonomous Mobile Robots in a Smart Factory
Christopher Leet, Aidan Sciortino, Sven Koenig
TL;DR
The paper tackles the problem of embedding a manufacturing procedure into a smart factory by jointly optimizing process-to-machine assignment and material transport planning for a team of mobile robots, with the goal of maximizing throughput. It introduces the Smart Factory Embedding Problem (SFEP) and the Anytime Cyclic Embedding Solver (ACES), a two-stage method that first solves a Fixed Cycle Length, Agent-Token SFEP (FLAT SFEP) as an MILP and then lifts the solution to a full SFEP embedding by iterating over cycle times Tc. A key innovation is modeling tokens as agent-tokens to reduce decision variables and using cyclic transport plans to enable indefinite looping, with ACES progressively exploring longer cycles under a time budget. Empirical results on six industrial scenarios demonstrate ACES's ability to produce scalable, high-throughput embeddings for realistic smart factory settings, highlighting potential gains for flexible manufacturing in practice.
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.