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基于改进NSGA-II算法的装配式建筑施工调度优化 被引量:8

A Construction Scheduling Optimization of Prefabricated Buildings Based on Improved NSGA-II Algorithm
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摘要 针对以往装配式建筑调度研究主要基于每项活动只有确定的活动时间和一种执行模式,而实际调度过程中存在不确定的活动时间和多种执行模式,建立多目标多模式资源约束下的模糊工期调度模型,提出一种改进的非支配排序遗传算法(INSGA-II)来求解(时间−成本)双目标优化模型。该算法根据活动的优先级关系进行种群初始化和交叉操作,同时提出新的包含活动列表、模式列表和资源列表的3段编码。最后,通过装配式建筑施工现场实际案例分析和算法性能对比,证明本文构建的调度模型和算法设计能有效地解决多模式资源约束下的模糊工期调度模型,为施工调度计划的设计提供科学的思路和方法。 In view of the previous studies on prefabricated building scheduling with only a certain activity time and one execution mode for each activity,while actual scheduling processes are with uncertain activity time and various execution modes of activities,a fuzzy scheduling model with multi-objective and multi-mode resource constraints is established.An improved non-dominated sorting genetic algorithm(INSGA-II)is proposed to solve the(time-cost)bi-objective optimization model.The proposed algorithm performs population initialization and crossover operations according to the priority relationship of activities,meanwhile,a new three-segment coding method is developed containing activity,mode and resource lists.Finally,through the case analysis of an actual prefabricated building construction site and the comparison of algorithm performance,it is proved that the proposed scheduling model and algorithm can effectively solve the fuzzy scheduling problem under multi-mode resource constraints.It provides scientific ideas and methods for the design of construction scheduling plan.
作者 汪和平 龚星霖 李艳 WANG Heping;GONG Xinglin;LI Yan(School of Management Science and Engineering,Anhui University of Technology,Maanshan 243002,China;School of Management,University of Shanghai for Science and Technology,Shanghai 200093,China)
出处 《工业工程》 北大核心 2023年第2期85-92,共8页 Industrial Engineering Journal
基金 国家自然科学基金资助项目(71872002) 教育部人文社会科学研究资助项目(19YJCZH091)。
关键词 资源约束项目调度问题 装配式建筑施工 INSGA-II算法 多目标优化 resource-constrained project scheduling problem(RCPSP) prefabricated building construction improved non-dominated sorting genetic algorithm(INSGA-II) multi-objective optimization
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