兰州新区二标段建设工程项目进度优化与控制

发布时间：2018-01-08 06:07

本文关键词：兰州新区二标段建设工程项目进度优化与控制　出处：《兰州交通大学》2014年硕士论文　论文类型：学位论文

【摘要】：目前,大部分的建设工程项目暴露出的管理问题主要集中在进度计划与费用目标不能较好协同均衡优化方面,为了达到建设工程项目总进度计划最优,建设工程成本最节约的要求;这篇文章正是基于这种考虑而形成的,首先主要把建设工程项目每一个施工过程的每一项施工活动的进度计划和费用作为一个研究对象,运用建设工程项目进度计划基本理论为支撑,逐步建立一个有关建设工程项目进度与费用均衡优化的数学模型,然后引入了蚁群遗传算法的改进算法解析出此类数学模型的最优化精确解;本篇论文重点从下面几个章节展开来叙述的:(1)通过对建设工程项目基本进度理论【涵盖工期计划理论、进度优化理论以及进度优化方法和控制等思想】以及人工智能算法中的蚁群遗传算法【蚁群算法、遗传算法、蚁群遗传融合算法以及蚁群遗传的改进算法】进行了系统的认真研读分析后,从整个理论框架上对进度计划基本理论和蚁群遗传的相关算法有了一个清晰认识和理解掌握。(2)通过分析建设工程项目中有关进度与费用优化的一系列问题(工程项目实际总进度计划滞后与合同工期目标,或者项目总进度计划达到了合同工期目标,但总费用超支合同造价的问题等)及其影响因素后,根据工程项目的实际情况和项目进度管理中存在的问题有针对性的提出了一个科学合理数学均衡优化模型;这种数学模型中应用到了线性代数,回归代数等。(3)本章节主要围绕建设工程项目的进度计划与费用均衡优化为目标,通过运用改进蚁群遗传算法的改进算法来对建设工程项目数学均衡优化模型求解,解析出建设工程项目进度计划与费用均衡优化的最优解,通过求解数学模型的最优解的过程来验证蚁群遗传改进算法的性能。(4)以兰州新区二标段建设工程项目为实例,通过查找施工合同以及建设工程项目进度计划科学分解进度计划和与之相应的造价资料后,整理出兰州新区二标段建设工程项目每一项施工任务的进度计划和费用目标,然后分别应用双代号网络计划图法与蚁群遗传融合改进算法对兰州新区二标段建设工程项目进度计划和费用均衡优化问题进行最优解求解,接着通过将两种算法获得的有关项目进度计划与费用的最优解的数据进行比对后,发现蚁群遗传算法的改进算法在建设工程项目进度计划优化方面优势较双代号网络图比较突出,另外蚁群遗传改进算法在费用优化方面还有进一步优化的空间,需要在以后的研究中逐步完善,便于后期的推广应用。(5)为了达成和巩固兰州新区二标段建设工程项目进度计划和费用均衡优化目标,并针对性制定了一系列的进度控制措施和保障计划;措施主要从组织保障措施、技术管理措施、信息与合同管理措施以及经济奖惩措施等四方面应对控制的。
[Abstract]:At present, most of the management problems exposed by the construction project mainly focus on the optimization of the schedule and the cost target. In order to achieve the optimal overall progress plan of the construction project. The most economical requirement of construction project cost; This article is based on this consideration, first of all, the construction project of each construction process of each construction activity schedule and cost as a research object. Based on the basic theory of progress plan of construction project, a mathematical model of equilibrium optimization of schedule and cost of construction project is established step by step. Then an improved ant colony genetic algorithm is introduced to analyze the optimal exact solution of this kind of mathematical model. This paper focuses on the introduction of the basic progress theory of construction project from the following chapters. Schedule optimization theory, schedule optimization methods and control ideas] and artificial intelligence algorithm in ant colony genetic algorithm [ant colony algorithm, genetic algorithm. Ant colony genetic fusion algorithm and improved ant colony genetic algorithm] have been carefully studied and analyzed. From the framework of the whole theory, we have a clear understanding and understanding of the basic theory of schedule planning and the related algorithms of ant colony genetics. This paper analyzes a series of problems related to the optimization of schedule and cost in construction project (the actual total schedule of the project is lagging behind and the target of the contract period is behind). Or the overall schedule of the project has reached the target of the contract, but the total cost overruns the cost of the contract and other issues) and its impact factors. According to the actual situation of the project and the problems existing in the project schedule management, a scientific and reasonable mathematical equilibrium optimization model is put forward. This mathematical model is applied to linear algebra, regression algebra, etc.) this chapter mainly focuses on the construction project schedule and cost balance optimization as the goal. By using the improved ant colony genetic algorithm to solve the mathematical equilibrium optimization model of the construction project, the optimal solution of the schedule and cost equilibrium optimization of the construction project is analyzed. The performance of the improved ant colony genetic algorithm is verified by the process of solving the optimal solution of the mathematical model. After finding out the construction contract and the progress plan of the construction project scientifically decomposing the progress plan and the corresponding cost data. The progress plan and cost target of each construction task of the second project of Lanzhou New District are sorted out. Then the double code network planning map method and ant colony genetic fusion improved algorithm are used to solve the project schedule and cost equilibrium optimization problem in Lanzhou New area. Then the data of the project schedule and the optimal solution of the cost obtained by the two algorithms are compared. It is found that the improved ant colony genetic algorithm has more advantages than the double symbol network in the optimization of construction project schedule. In addition, there is room for further optimization in the cost optimization of the improved ant colony genetic algorithm. In order to achieve and consolidate the Lanzhou New District No. 2 construction project schedule and cost balance optimization objectives. And has made a series of progress control measures and the safeguard plan pertinently; The measures should be controlled from four aspects: organization safeguard measure, technical management measure, information and contract management measure and economic reward and punishment measure.
【学位授予单位】：兰州交通大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TU722

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