Jointly Assigning Processes to Machines and Generating Plans for Autonomous Mobile Robots in a Smart Factory
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866929735560331264 |
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| author | Leet, Christopher Sciortino, Aidan Koenig, Sven |
| author_facet | Leet, Christopher Sciortino, Aidan Koenig, Sven |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_21101 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Jointly Assigning Processes to Machines and Generating Plans for Autonomous Mobile Robots in a Smart Factory Leet, Christopher Sciortino, Aidan Koenig, Sven Robotics Optimization and Control 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. |
| title | Jointly Assigning Processes to Machines and Generating Plans for Autonomous Mobile Robots in a Smart Factory |
| topic | Robotics Optimization and Control |
| url | https://arxiv.org/abs/2502.21101 |