多样性(控制论)
声誉
新颖性
任务(项目管理)
过程(计算)
知识管理
集合(抽象数据类型)
组织结构
计算机科学
生产(经济)
技术变革
管理科学
工程类
管理
经济
系统工程
宏观经济学
程序设计语言
人工智能
社会学
哲学
操作系统
社会科学
神学
作者
Anastasia Igorevna Abramova
标识
DOI:10.1051/e3sconf/202125809063
摘要
An important task in the construction of buildings and structures is the choice of the most rational solutions in terms of organization and technology of construction production. Currently, the complexity and variety of organizational and technological decisions in the dynamics of the process of erecting buildings and structures for various purposes requires research. To this end, it is necessary to improve existing or develop new organizational and technological solutions, in conditions when the tasks are set to increase the growth in the volume of housing construction. The author is faced with the task of forming an integrated system that includes organizational and technological solutions, methods and assessment of the effectiveness of the study. It is assumed that the results of the study will be in demand by construction organizations to maintain competitiveness, paying attention to organizational and technological solutions, both at the stage of preparation and during construction. This will prevent losses, increase the effective functioning of construction organizations in the market, and strengthen their business reputation. Scientific novelty lies in the formation of parameters and their classification, methods of modeling organizational and technological solutions. To assess organizational and technological solutions, it is necessary to study methods and ways of measuring them. In the course of the research, an analysis of the methods widely used at present for the qualitative and quantitative assessment of organizational and technological solutions was carried out. Qualitative assessment methods make it possible to reveal the significance of certain factors of a phenomenon through the analysis of competent opinions. In the case of studying the influence of organizational and technological solutions on construction processes, there are no measurable physical parameters, objects inaccessible to perception and large volumes of statistical data. The article concludes that empirical, theoretical and quantitative methods are difficult for the research chosen by the author. Therefore, the author of this article chose the Monte Carlo method (MCM) as a research method - a group of numerical methods for studying random processes. Also, the work identified a vector for further research.
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