AbstractThe construction industry has been suffering from delays and cost overruns for decades. Experienced schedulers programs and allocate contingencies (both cost and time) based on professional experiences and gained knowledge. Such tacit knowledge has not been captured, stored, and shared with inexperienced schedulers. This paper proposes a graph-based automated scheduling (GAS) method to capture, store, and reuse the tacit knowledge in the construction schedules. The proposed GAS method takes construction schedules as input, extracts schedule features, classifies schedules into different types of sequences, selects and assembles sequences into schedules, and eventually optimizes time- and cost-efficiency of assembled schedules. The GAS method was validated on two case studies. The results indicated that the automatically generated construction schedule is, on average, 6.70% closer to the actual schedule than the planned schedule. Different from existing automatic scheduling methods, GAS relies little on the availability and data richness of building information modeling (BIM) models. Hence, GAS helps schedulers initiate new schedules more efficiently at the early construction stages.

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