How to effectively optimize the operation for a complex supply chain environment has been high on the agenda. Although the existing deep reinforcement learning methods have achieved success in certain applications, they still face limitations in complex supply chain environments, including difficulties in data sharing and a lack of digital collaboration, especially in multi-agent systems. In order to meet this challenge, we propose a novel multi-agent reinforcement learning algorithm based on dynamic optimistic weights (DO-QMIX), aiming at solving the shortcomings of the traditional weighted QMIX algorithm (WQMIX) in the simplicity of the weighting function. WQMIX employs two weighting schemes to handle multi-agent issues. However, its fixed weighting function restricts algorithm performance and hinders adaptability to dynamic, complex supply chain challenges. Therefore, we propose a dynamic weighting mechanism, which can adjust the weighting function in real time based on the changes in the environment, thus improving the overall efficiency. We construct a complex multi-stage supply chain environment in the real-world supply chain scenario and conduct many experiments using both real-world and simulated datasets. The experimental results demonstrate that DO-QMIX is significantly superior to the traditional multi-agent reinforcement learning algorithm in complex supply chain scenarios, especially in dealing with dynamic changes and complex decisions.

Scatterplots are widely used to visualize scatter dataset for exploring outliers, clusters, local trends, and correlations. Depicting multi-class scattered points within a single scatterplot view, however, may suffer from heavy overdraw, making it inefficient for data analysis. This paper presents a new visual abstraction scheme that employs a hierarchical multi-class sampling technique to show a feature-preserving simplification. To enhance the density contrast, the colors of multiple classes are optimized by taking the multi-class point distributions into account. We design a visual exploration system that supports visual inspection and quantitative analysis from different perspectives. We have applied our system to several challenging datasets, and the results demonstrate the efficiency of our approach.

Proposes a method to learn the typical assembly operation of inserting a flexible wire into hole. The input space is divided into different contexts. Several virtual agents based on a learning automaton are constructed in the output space. Through learning, the agents can learn optimal actions according to different contexts, and achieve the insertion task together. The paper also proposes a computation approach based on a multi-body model to simulate the insertion process. The simulation results of a 2D insertion operation prove the feasibility of the proposed methods.