重复性项目前摄性调度优化方法研究

doi:10.3969/j.issn.1007-7375.230013

摘要/Abstract

摘要： 为应对重复性项目在建设过程中遇到的不确定性干扰，需对重复性项目进行前摄性调度优化研究，以增加项目进度计划容纳不确定性因素干扰的能力。本文首先分析基于线性计划法下，重复性项目中线状、条状、块状活动之间的时差关系，通过时差大小确定缓冲区间的选择范围。然后以工期、成本、鲁棒性为目标，构建多目标前摄性优化调度模型。并针对其NP-hard属性，设计改进粒子群优化算法：通过引入模拟退火算法，避免粒子群容易陷入局部最优的问题，算法测试表明改进算法具有较快的运算速度和较好的寻优能力。最后，通过一个重复性工程的案例，验证本文提出的前摄性调度优化模型的可行性和有效性；并通过仿真模拟发现，本文提出方法消耗更少缓冲就可以保证项目按时完工。通过研究分析得到以下结论：在编制进度计划时，可以通过在一定范围内增加资源量的方式，获得更优的进度计划。

关键词: 重复性项目, 前摄性项目调度, 缓冲设置, 粒子群算法 (PSO)

Abstract: In order to cope with uncertain disturbance encountered in the construction process of repetitive projects, proactive scheduling optimization of repetitive projects is necessary to increase the capacity of project schedules to accommodate uncertainties. In this paper, the float relationships among linear, bar and block activities in repetitive projects is first analyzed based on linear planning. The selection ranges between buffers are determined according to floats. Then, a multi-objective proactive scheduling model is established with the objectives of project duration, cost and robustness. To address its NP-hard nature, a modified particle swarm optimization algorithm (i.e., SA-PSOc) is designed, which incorporate simulated annealing algorithm to avoid particles easily falling into local optima. Algorithm testing shows that the modified algorithm has better global search ability and faster computational speed. Finally, the feasibility and effectiveness of the proposed proactive scheduling optimization model are verified by a case study of a repetitive project. Through simulation, it demonstrates that the proposed method consumes fewer buffers to ensure the project completion on time. Conclusion is made that when preparing a schedule, by increasing resources within a certain range, a better schedule can be achieve.

Key words: repetitive projects, proactive project scheduling, buffer settings, particle swarm optimization (PSO)

中图分类号:

周国华, 夏晶. 重复性项目前摄性调度优化方法研究[J]. 工业工程, 2024, 27(3): 31-42.

ZHOU Guohua, XIA Jing. Proactive Project Scheduling Optimization of Repetitive Projects[J]. Industrial Engineering Journal, 2024, 27(3): 31-42.

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