Embedding ensemble learning into simulation-based optimisation: a learning-based optimisation approach for construction planning

Abstract

PurposeSimulation-based optimisation (SO) is a popular optimisation approach for building and civil engineering construction planning. However, in the framework of SO, the simulation is continuously invoked during the optimisation trajectory, which increases the computational loads to levels unrealistic for timely construction decisions. Modification on the optimisation settings such as reducing searching ability is a popular method to address this challenge, but the quality measurement of the obtained optimal decisions, also termed as optimisation quality, is also reduced by this setting. Therefore, this study aims to develop an optimisation approach for construction planning that reduces the high computational loads of SO and provides reliable optimisation quality simultaneously.Design/methodology/approachThis study proposes the optimisation approach by modifying the SO framework through establishing an embedded connection between simulation and optimisation technologies. This approach reduces the computational loads and ensures the optimisation quality associated with the conventional SO approach by accurately learning the knowledge from construction simulations using embedded ensemble learning algorithms, which automatically provides efficient and reliable fitness evaluations for optimisation iterations.FindingsA large-scale project application shows that the proposed approach was able to reduce computational loads of SO by approximately 90%. Meanwhile, the proposed approach outperformed SO in terms of optimisation quality when the optimisation has limited searching ability.Originality/valueThe core contribution of this research is to provide an innovative method that improves efficiency and ensures effectiveness, simultaneously, of the well-known SO approach in construction applications. The proposed method is an alternative approach to SO that can run on standard computing platforms and support nearly real-time construction on-site decision-making.

Design of a Digital Twin in Low-Volume, High-Mix Job Allocation and Scheduling for Achieving Mass Personalization

“…Furthermore, advancements in technology have augmented the capabilities of SBO. The integration of machine learning and artificial intelligence can enhance the performance of SBO by improving predictive accuracy, reducing computational times, and aiding in decision-making processes [29,30]. As a representative machine learning algorithm, deep neural networks (DNNs) have recently gained traction in tackling scheduling problems.…”

Energy-Efficient Retrofitting under Incomplete Information: A Data-Driven Approach and Empirical Study of Sweden

“…The current cornerstone technologies behind the data-driven approach include Internet, digital devices, and computer-aid simulation (all of them for data generation and collection), as well as computer science, such as machine learning and data mining (all of them for analysis and modelling) [32]. It is recognized that the data-driven approach can be applied to many different aspects, such as regional building energy requirement forecasting [33], the building design performance modelling [27], the scheme planning during building construction [34], building design optimization [35], and building retrofitting [36,37].…”