While time series data are prevalent across diverse sectors, data labeling process still remains resource-intensive. This results in a scarcity of labeled data for deep learning, emphasizing the importance of semi-supervised learning techniques. Applying semi-supervised learning to time series data presents unique challenges due to its inherent temporal complexities. Efficient contrastive learning for time series requires specialized methods, particularly in the development of tailored data augmentation techniques. In this paper, we propose a single-step, semi-supervised contrastive learning framework named nearest neighbor contrastive learning for time series (NNCLR-TS). Specifically, the proposed framework incorporates a support set to store representations including their label information, enabling a pseudo-labeling of the unlabeled data based on nearby samples in the latent space. Moreover, our framework presents a novel data augmentation method, which selectively augments only the trend component of the data, effectively preserving their inherent periodic properties and facilitating effective training. For training, we introduce a novel contrastive loss that utilizes the nearest neighbors of augmented data for positive and negative representations. By employing our framework, we unlock the ability to attain high-quality embeddings and achieve remarkable performance in downstream classification tasks, tailored explicitly for time series. Experimental results demonstrate that our method outperforms the state-of-the-art approaches across various benchmarks, validating the effectiveness of our proposed method.

As semiconductor manufacturers, recently, have focused on producing multichip products (MCPs), scheduling semiconductor manufacturing operations become complicated due to the constraints related to reentrant production flows, sequence-dependent setups, and alternative machines. At the same time, the scheduling problems need to be solved frequently to effectively manage the variabilities in production requirements, available machines, and initial setup status. To minimize the makespan for an MCP scheduling problem, we propose a setup change scheduling method using reinforcement learning (RL) in which each agent determines setup decisions in a decentralized manner and learns a centralized policy by sharing a neural network among the agents to deal with the changes in the number of machines. Furthermore, novel definitions of state, action, and reward are proposed to address the variabilities in production requirements and initial setup status. Numerical experiments demonstrate that the proposed approach outperforms the rule-based, metaheuristic, and other RL methods in terms of the makespan while incurring shorter computation time than the metaheuristics considered.

In this paper, we introduce LiveGantt as a novel interactive schedule visualization tool that helps users explore highly-concurrent large schedules from various perspectives. Although a Gantt chart is the most common approach to illustrate schedules, currently available Gantt chart visualization tools suffer from limited scalability and lack of interactions. LiveGantt is built with newly designed algorithms and interactions to improve conventional charts with better scalability, explorability, and reschedulability. It employs resource reordering and task aggregation to display the schedules in a scalable way. LiveGantt provides four coordinated views and filtering techniques to help users explore and interact with the schedules in more flexible ways. In addition, LiveGantt is equipped with an efficient rescheduler to allow users to instantaneously modify their schedules based on their scheduling experience in the fields. To assess the usefulness of the application of LiveGantt, we conducted a case study on manufacturing schedule data with four industrial engineering researchers. Participants not only grasped an overview of a schedule but also explored the schedule from multiple perspectives to make enhancements.