Flexible Scheduling Method Research Articles

A digital twin-driven flexible scheduling method in a human-machine collaborative workshop based on hierarchical reinforcement learning.

Under the influence of the global COVID-19 pandemic, the demand for medical equipment and epidemic prevention materials has increased significantly, but the existing production lines are not flexible and efficient enough to dynamically adapt to market demand. The human-machine collaboration system combines the advantages of humans and machines, and provides feasibility for implementing different manufacturing tasks. With dynamic adjustment of robots and operators in the production line, the flexibility of the human-machine collaborative production line can be further improved. Therefore, a parallel production line is set up as a parallel community, and the digital twin community model of the intelligent workshop is constructed. The fusion and interaction between the production communities enhance the production flexibility of the manufacturing shop. Aiming at the overall production efficiency and load balancing state, a digital twin-driven intra-community process optimization algorithm based on hierarchical reinforcement learning is proposed, and as a key framework to improve the production performance of production communities, which is used to optimize the proportion of human and machine involvement in work. Finally, taking the assembly process of ventilators as an example, it is proved that the intelligent scheduling strategy proposed in this paper shows stronger adjustment ability in response to dynamic demand as well as production line changes.

Applying Queuing Theory and Mixed Integer Programming to Blood Center Nursing Schedules of a Large Hospital in China.

Blood centers in large hospitals in China are facing serious problems, including complex patient queues and inflexible nursing schedules. This study is aimed at developing a flexible scheduling method for blood center nurses. By systematically analyzing the constraints that affect scheduling, a flexible scheduling model is established based on queuing theory and mixed integer programming. This combined model can reasonably determine the number of nurses required during a given working period and flexibly arrange nursing schedules while ensuring sufficient rest periods for individual nurses. Results of numerical studies conducted using data from a large hospital in China show a significant improvement in patient waiting time performance metrics over the hospital's current practice. In addition, the nurses' workloads and rest periods are well balanced, indicating that the proposed method can effectively and flexibly arrange nursing shifts in blood centers.

AI-Enabled Massive Devices Multiple Access for Smart City

Smart city is coming into urban life with the development of information and communication technologies. As an integral part of the smart city, massive heterogeneous Internet of Things devices face enormous challenges in multiple accessing and signal processing. In this paper, we propose an innovative and flexible scheduling method under the channel field multiple access framework to meet these requirements. In particular, artificial intelligence (AI) technology is adopted by the proposed solution. It is proved that the proposed AI-based scheduling method can obtain better performance than the existing methods by low complexity, especially, such performance gain becomes more significant as the number of devices increases.

Improving Perceived Fairness of Task Assignments in Cardiovascular Intensive Nursing Care Unit with Simple Queuing Mechanism

In this work, a simple computer application is proposed to improve the perceived fairness of Cardiovascular Intensive Care Unit (CVICU) nursing team in hospitals. The proposed system called Intensive care Unit (ICU) Task Assignment Platform (ICU-TAP) is aimed to increase perceived fairness and satisfaction through a systematic but flexible scheduling method. The solution proposed here takes into account the skill preference of each nurse to ensure an equal opportunity to maintain career goals, with an aim to increasing job satisfaction. The system is designed based on the basic concept of queuing. ICU-TAP equally distributes the harder and monotonic tasks among nurses and takes into account the desired skill of each nurse for more frequent assignment of other tasks. The proposed system is expected to make the workflow of the unit traceable, resulting in paperless scheduling, and reducing discrepancies between scheduling and daily task assignment of the nursing unit.

Scheduling distributed real-time tasks with minimum jitter

The problem of scheduling real-time tasks with minimum jitter is particularly important in many control applications; nevertheless, it has rarely been studied in the scientific literature. This paper presents an unconventional scheduling approach for distributed static systems where tasks are periodic and have arbitrary deadlines, precedence, and exclusion constraints. The solution presented in this work not only creates feasible schedules, but also minimizes jitter for periodic tasks. We present a general framework consisting of an abstract architecture model and a general programming model. We show how to design a surprisingly simple and flexible scheduling method based on simulated annealing. Experimental results demonstrate the significant improvement of our algorithm over earliest deadline first and rate monotonic algorithms.