Individualized products and shorter product life cycles have driven companies
to rethink traditional mass production. New concepts like Industry 4.0 foster
the advent of decentralized production control and distribution of information.
A promising technology for realizing such scenarios are Multi-agent systems.
This contribution analyses the requirements for an agent-based decentralized
and integrated scheduling approach. Part of the requirements is to develop a
linearly scaling communication architecture, as the communication between the
agents is a major driver of the scheduling execution time. The approach
schedules production, transportation, buffering and shared resource operations
such as tools in an integrated manner to account for interdependencies between
them. Part of the logistics requirements reflect constraints for large
workpieces such as buffer scarcity. The approach aims at providing a general
solution that is also applicable to large system sizes that, for example, can
be found in production networks with multiple companies. Further, it is
applicable for different kinds of factory organization (flow shop, job shop
etc.). The approach is explained using an example based on industrial
requirements. Experiments have been conducted to evaluate the scheduling
execution time. The results show the approach's linear scaling behavior. Also,
analyses of the concurrent negotiation ability are conducted.

本篇研究针对工业 4.0 的要求，采用多智能体系统实现了分散化生产控制和信息分配，提出了一种基于智能代理的集成调度方法，该方法解决了在多智能体系统下排班执行时间过长的问题，并考虑到了生产、运输、缓冲和共享资源操作的相互依赖性及大型工件的约束等物流需求。

基于 Agent 的生产和辅助过程去中心化集成调度的概念和算法

Concepts and Algorithms for Agent-based Decentralized and Integrated Scheduling of Production and Auxiliary Processes

The ever-increasing heavy traffic congestion potentially impedes the
accessibility of emergency vehicles (EVs), resulting in detrimental impacts on
critical services and even safety of people's lives. Hence, it is significant
to propose an efficient scheduling approach to help EVs arrive faster. Existing
vehicle-centric scheduling approaches aim to recommend the optimal paths for
EVs based on the current traffic status while the road-centric scheduling
approaches aim to improve the traffic condition and assign a higher priority
for EVs to pass an intersection. With the intuition that real-time vehicle-road
information interaction and strategy coordination can bring more benefits, we
propose LEVID, a LEarning-based cooperative VehIcle-roaD scheduling approach
including a real-time route planning module and a collaborative traffic signal
control module, which interact with each other and make decisions iteratively.
The real-time route planning module adapts the artificial potential field
method to address the real-time changes of traffic signals and avoid falling
into a local optimum. The collaborative traffic signal control module leverages
a graph attention reinforcement learning framework to extract the latent
features of different intersections and abstract their interplay to learn
cooperative policies. Extensive experiments based on multiple real-world
datasets show that our approach outperforms the state-of-the-art baselines.

LEVID 是一种学习驱动的协作车路调度方法，包括实时路径规划和协作交通信号控制模块，能够相互交互并迭代地做出决策，实验证明其优于现有的基线方案。

学习协作式车辆路线调度方法，助力应急车辆快速到达

Learning to Help Emergency Vehicles Arrive Faster: A Cooperative Vehicle-Road Scheduling Approach

The optimal scheduling of interfering links in a dense wireless network with
full frequency reuse is a challenging task. The traditional method involves
first estimating all the interfering channel strengths then optimizing the
scheduling based on the model. This model-based method is however resource
intensive and computationally hard because channel estimation is expensive in
dense networks; furthermore, finding even a locally optimal solution of the
resulting optimization problem may be computationally complex. This paper shows
that by using a deep learning approach, it is possible to bypass the channel
estimation and to schedule links efficiently based solely on the geographic
locations of the transmitters and the receivers, due to the fact that in many
propagation environments, the wireless channel strength is largely a function
of the distance dependent path-loss. This is accomplished by unsupervised
training over randomly deployed networks, and by using a novel neural network
architecture that computes the geographic spatial convolutions of the
interfering or interfered neighboring nodes along with subsequent multiple
feedback stages to learn the optimum solution. The resulting neural network
gives near-optimal performance for sum-rate maximization and is capable of
generalizing to larger deployment areas and to deployments of different link
densities. Moreover, to provide fairness, this paper proposes a novel
scheduling approach that utilizes the sum-rate optimal scheduling algorithm
over judiciously chosen subsets of links for maximizing a proportional fairness
objective over the network. The proposed approach shows highly competitive and
generalizable network utility maximization results.

本篇研究论文讨论如何使用深度学习方法，通过利用发射机和接收机之间的地理位置，不需要通道估计，对干扰链路的优化调度算法，提出了一种新的神经网络结构。并针对现有的基于模型的方法提出了相对公正的基于比例的调度方法。实际测试表明，该方法可适用于大型网络和不同链路密度的部署，并有效地提高了网络效用。