# The Artificial Intelligence-Driven Intelligent Laboratory for Organic Chemistry Synthesis
## 2 Laboratory Equipment and Intelligent Systems
### Intelligent Coordination and Scheduling
#### 2.2.2 The Scheduling of Robot Swarms  
In intelligent chemical laboratories, multiple autonomous mobile robots are often relied upon to transport reaction materials and products, as well as to execute various operations. The scheduling of robot swarms in this context is crucial for the overall efficiency of the system. A well-designed scheduling strategy can significantly enhance experimental efficiency, reduce operational costs, and improve system reliability. Robot \begin{table}
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**Research Team** & **Method/System** & **Key Features** \\ \hline [PERSON] et al. [31] & Multi-production line intelligent manufacturing plant optimization model & A Mixed Integer Linear Programming (MILP) model incorporating constraints such as energy consumption, cost, productivity, and scrap rate, supports optimal scheduling for multi-product production lines. \\ [PERSON] et al. [32] & Deep reinforcement learning scheduler for chemical production & A deep reinforcement learning framework combined with a forecasting mechanism and scaled MILP model, designed for handling dynamic uncertainties in single-stage, multi-product reactors. An MILP formulation minimizing synthesis time, integrating delay times, module capacities, and shift constraints to optimize linear and tree-structured synthesis pathways. \\ \hline \end{tabular}
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Table 3: Research in Production Scheduling. swarm control is a significant topic in robotics research due to its wide range of applications and consistent research interest. For the scheduling of mobile robot swarms, [PERSON] et al. proposed a multi-robot and multi-task scheduling system [34] specifically designed for autonomous chemical laboratories to improve the efficiency of executing complex chemical experiments. The system first analyzes task sequences and laboratory resources to obtain the FESP-B (Flexible Experimental Scheduling Problem-B) model, then uses constraint programming algorithms to solve the FESP-B problem by adding constraints based on task sequences, constructing them into a tree structure, and using heuristic searches to find the optimal solution. Due to the similarity between robot swarm scheduling and vehicle routing problems, researchers often employ vehicle routing methods to address robot swarm scheduling issues. [PERSON] et al. implemented a method for coordinating robot swarms [35] that is robust and fast. The core of this method is a distributed task allocation and scheduling algorithm that addresses time and priority constraints between tasks, representing the problem as a variant of the vehicle routing problem, and using a distributed metaheuristic algorithm based on evolutionary computation (CBM-pop) to find solutions. Compared to traditional methods, this approach offers the advantage of quickly and proximally assigning tasks. Artificial intelligence technologies such as neural networks, with their high-dimensional and nonlinear characteristics, are often applied to various complex industrial scenarios. [PERSON] et al. proposed a robot collaborative scheduling strategy learning scheme based on graph attention networks [36]. This scheme combines imitation learning and graph neural network algorithms within a non-parametric framework, allowing for rapid and approximately optimal scheduling of robot swarms of various sizes, significantly accelerating computational efficiency while ensuring the approximate nature of the scheduling plan. [PERSON] et al. introduced a scheduling system for solving the multi-robot flexible job shop scheduling (MRFJS) problem with limited buffers [37]. The system includes a mixed integer linear programming (MILP) model to minimize completion time and an improved genetic algorithm to find the optimal scheduling scheme. Experiments indicate that this scheme significantly outperforms traditional genetic-based scheduling schemes in terms of relative deviation and other aspects.