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基于运架匹配的铁路工程预制梁场选址模型与优化

Railway Engineering Prefabricated Beam Fields Location Model and Optimization Based on the Matching of Transportation and Erecting Process
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摘要 预制梁场作为铁路工程的大型临时设施,其数量和位置的优化决策对工程成本控制意义重大。针对铁路工程特征,研究基于线性调度方法的选址问题,在已有研究的基础上补充了10种情形描述线性工程的最小工期约束。构建以成本为导向的运架匹配预制梁场选址优化模型,设计引入灾变算子的改进粒子群-遗传算法,对梁场的数量、位置、架梁方向和开工时序进行优化决策。实例研究表明:模型和算法能有效减少梁场数量、降低工程成本,获得较原方案更优的梁场选址方案,为预制梁场全线选址问题提供决策依据。 As a large temporary facility of railway project,the decision of prefabricated beam fields'number and location is important to the project cost control.The characteristics of railway engineering were considered.The location problem was studied based on the linear scheduling method(LSM).Ten scenarios were proposed to describe the minimum duration constraint of linear engineering based on previous study.Considering the matching of transportation and erecting process,a cost-oriented location optimization model of prefabricated beam fields was proposed,and an improved particle swarm-genetic algorithm with catastrophic operator was designed to make optimal decisions on the number,location,erecting direction,and erecting time of beam fields.The example study shows that the model and algorithm can effectively reduce the number of beam fields and project cost.A better beam fields location plan than the original one was obtained.It can provide methodological support for the whole-line prefabricated beam fields location problem.
作者 周国华 陈雪玉 魏强 卢春房 赵健 王鹏 ZHOU Guohua;CHEN Xueyu;WEI Qiang;LU Chunfang;ZHAO Jian;WANG Peng(School of Economics and Management,Southwest Jiaotong University,Chengdu,Sichuan 610031,China;China Railway Construction Management Co.Ltd.,Beijing 100000,China;China Railway Society,Beijing 100844,China)
出处 《工业工程与管理》 CSCD 北大核心 2024年第2期130-139,共10页 Industrial Engineering and Management
基金 国家自然科学基金重大专项(71942006) 中国国家铁路集团有限公司科技研究开发计划重大课题(K2022G00)。
关键词 工程管理 线性工程 选址问题 多梁场 改进粒子群-遗传算法 决策优化 engineering management linear project location problem multiple prefabricated beam fields improved particle swarm-genetic algorithm decision optimization
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