Search Results (2)

To maximize resource usage, minimize disputes, and improve academic experience, professors must schedule teaching activities efficiently. This study provides an optimized automated schedule creation technique. The system generates schedules that aim to be conflict-free and efficient, utilizing evolutionary algorithms along with multi-objective optimization. Resource usage, scheduling problems, and faculty/student satisfaction are the goals of the research. The system optimizes scheduling based on room limitations, instructor availability, and student preferences. The project uses system design, model creation, algorithm implementation, and performance analysis to solve the difficult timetable-generating problem. This research should save administrators time, improve academic operations, and improve staff and student academic experiences. Scalability and flexibility allow the system to be used in multiple faculties and incorporate new limits and requirements. This paper presents a complete approach to faculty scheduling, including insights and recommendations for future study and application in educational institutions. Full article

The goal of train re-scheduling is redesigning the time when trains arrive at and depart from stations of a railway section, and train control problem refers to determining the operating mode for a train in a railway section. It is quite necessary to study the two problems together, and they can be viewed as a theory base for self-driving study. We build a novel model to deal with train re-scheduling and train control problem synthetically. The approach is divided into two stages. The first stage is train re-scheduling, determining the arrival and departure time for trains. Depending on the arrival and departure time, the train running time can be calculated and it is set to be the constraint of the train control model. The destination of the second stage model is to save tracking energy in train operation process, determining the traction plan in each segment of a section between two stations. We also design a quantum-inspired particle swarm optimization algorithm to solve the integrated model. A computation case is presented to prove the availability of the approach. It can generate the re-scheduled timetable and train control plan synthetically with the approach presented in this paper. The main contribution of this paper is to propose a novel approach to solve train re-scheduling problem and train control problem synthetically. It can also provide supporting information for both the dispatchers and the train drivers to improve the on schedule rate and reduce the energy consumption. Furthermore, it may provide some valuable reference for the realization of automatic train driving. Full article