Optimized variable resource allocation framework for scheduling of fast-track industrial construction projects

Abstract

Efficient scheduling and resource allocation for large-scale industrial projects is challenging due to their size and complexity, especially with fast-track contracts, which often lack detailed information during the early planning phase. This paper introduces a data-driven workface planning framework to enhance scheduling and resource allocation while accommodating uncertainties and constraints (e.g., minimum and maximum resource allocation curves, dynamic predecessor relationships, congestion limits). This framework employs an integrated approach, combining time-stepped simulation with graph-based optimization. By leveraging historical data and expert knowledge, the data-driven framework mitigates certain subjective assumptions, including durations and resource allocations. In practical application, the framework generates near-optimal schedules, even with limited information. Applying the framework to a fast-track industrial construction project case study demonstrated enhanced resource allocation. These findings offer practical benefits to industrial projects regarding time and cost savings and serve as a foundation for future research in data-driven project planning approaches.