Automating Pipe Spool Fabrication Shop Scheduling for Modularized Industrial Construction Projects Using Reinforcement Learning

Industrial projects are primarily constructed using a modularized and prefabricated approach. Modules are produced in an offsite fabrication shop and then transported to the construction site for installation. Thus, timely and sequence-specific delivery of preassembled construction elements is essential to prevent delays and ensure a smooth construction progress. As such, fabrication shop schedules are crucial for the success of the entire construction project. Unlike a manufacturing fabrication shop, a construction fabrication shop fabricates unique engineer-to-order products, resulting in challenging shop schedules that involve several conditions and constraints, including material availability, processing time, resource availability, and due dates. Further, the manual iterative nature of the scheduling process makes it laborious and time-consuming, especially when it happens on a frequent basis. This paper presents a deep reinforcement learning (DRL) model for automating the scheduling process. The scheduling process is formulated as a Markov decision process (MDP); then, DRL is used to solve the MDP efficiently for a fabrication shop with large state space. The model is tested on a data set from a pipe spool fabrication shop located in Alberta, Canada; the results show that the DRL outperforms the most popular dispatching rules. This study serves as a first attempt, to our best knowledge, to automate the scheduling process using DRL, thus creating a solid foundation for future advancement in automating and optimizing construction scheduling.