Digital Commons Network^™

A Deep Reinforcement Learning Approach With Prioritized Experience Replay And Importance Factor For Makespan Minimization In Manufacturing, Jose Napoleon Martinez

LSU Doctoral Dissertations

In this research, we investigated the application of deep reinforcement learning (DRL) to a common manufacturing scheduling optimization problem, max makespan minimization. In this application, tasks are scheduled to undergo processing in identical processing units (for instance, identical machines, machining centers, or cells). The optimization goal is to assign the jobs to be scheduled to units to minimize the maximum processing time (i.e., makespan) on any unit.

Machine learning methods have the potential to "learn" structures in the distribution of job times that could lead to improved optimization performance and time over traditional optimization methods, as well as to adapt …

Automated Generation And Visualization Of Initial Construction Schedules From Building Information Models, Yibrah Weldemihret Weldu

LSU Doctoral Dissertations

Recent advances in digital technology have had a significant influence on the quality and speed of sharing and communicating project information in the architecture, engineering, and construction (AEC) industry. The process of acquiring the design intent in order to develop and communicate project schedules, as critical components of project delivery, have similarly been benefitting from such progress. With the relatively recent techniques of Building Information Modeling (BIM) and its capability to integrate the facility design with its construction schedule, meaningul strides have been made in improving the information flow and eventually visualizing the final schedule in 4D. However, the need …