首次联合优化机器分配与机器人路径，提升智能工厂产能。
Jointly Assigning Processes to Machines and Generating Plans for Autonomous Mobile Robots in a Smart Factory
- 联合求解工序分配与机器人路径规划
- 在真实工业场景下可扩展，显著提升吞吐量
- 适合智能制造系统优化的研究与工程人员
现代智能工厂通过一系列可编程机器运行制造流程，物料通常由移动机器人团队在机器间运输。将制造流程嵌入智能工厂需完成两步：一是将工序分配给机器，二是确定机器人搬运物料的路径。优良的嵌入方案能最大化工厂吞吐率。现有管理系统按顺序解决这两个问题，限制了整体性能。本文提出ACES（Anytime Cyclic Embedding Solver），首个可联合优化工序分配与机器人路径的求解器。我们在真实工业场景中评估了ACES，验证其具备良好的可扩展性与实际应用潜力。
原文摘要 · Abstract (English)
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.
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