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Dynamic scheduling optimization model and algorithm for linear projects considering local rescheduling
DOI:10.1108/ECAM-07-2024-0986.png)
Abstract
En 中文
PurposeLinear projects often involve lengthy construction periods, necessitating dynamic adjustments to the plan. Completely rescheduling remaining activities every time can lead to unnecessary time and cost wastage and significant deviations in resource supply. To address these issues, this paper proposes a dynamic scheduling method designed to effectively manage both time and cost during construction projects.Design/methodology/approachDetermining the rescheduling frequency through a hybrid driving strategy and buffer mechanism, introducing rolling window technology to determine the scope of local rescheduling and constructing a local rescheduling model under the constraints of time and cost deviation with the objective of minimizing the cost. Combined decision-making for construction and rushing modes constrained by multiple construction scenarios. Opposite learning is introduced to optimize the hybrid algorithm solution.FindingsArithmetic examples and cases confirm the model's feasibility and applicability. The results indicate that (1) continuous rescheduling throughout project construction is essential and effective and (2) a well-structured buffer mechanism can prevent redundant rescheduling and enhance overall control of cost and schedule deviations.Originality/valueThis study introduces an innovative dynamic scheduling framework for linear engineering, offering a method for effectively controlling schedule deviations during construction. The developed model enhances rescheduling efficiency and introduces a combined quantization strategy to increase the model's applicability to linear engineering. This model emerges as a promising decision support tool, facilitating the implementation of sustainable construction scheduling practices.
Keywords:
Local rescheduling
Linear scheduling method
Opposite learning
Time and cost deviations
Journal
J
IF:
5.1
Papers:
5.1K
Citations:
1.4W
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