Multi-objective bilevel construction material transportation scheduling in large-scale construction projects under a fuzzy random environment

2013 ◽  
Vol 36 (4) ◽  
pp. 352-376 ◽  
Author(s):  
Jiuping Xu ◽  
Jun Gang
2016 ◽  
Vol 4 (2) ◽  
Author(s):  
Putera Kumarayasa Mudita ◽  
I K. Sudarsana ◽  
Mayun Nadiasa

Abstract :When undertaking construction projects, the smoothflow of materials to the project site must be maintained.Waiting for material, which frequently happens, will have a big impact, especially for large-scale projects that use a lot of labor. If the arrival of the materials is not in accordance with the schedule of material procurement planning, the workers will have nothing to do and the project cost will blow out and there will be delays in project completion time. This research investigates the factors which influence the lead time of construction material procurement in the Badung Regency. Data was obtained by distributing a questionnaire to 50 respondents. A research sample was obtained by purposive sampling aimed at the experts who work on building projects in the Badung regency. Before being used as a research instrument, the questionnaire was tested for the validity of data by using the Pearson Product-Moment correlation and its reliability was tested using the Cronbach alpha method. Processing and data analysis was conducted by Factor Analysis. The research results show there are twenty four variables identified that influence the lead time of construction material procurement on building projects in the Badung regency. All variables can be grouped into seven factors (Factor I, II, III, IV, V, VI, VII). The most dominant factor reviewed based on the percent of variance is Factor I which is formed by six variables being the material production process at the suppliers, the relationships between contractors and suppliers, a lack of material stock at the suppliers, the availability of material transportation, access to the project, and extreme topography.


2021 ◽  
Author(s):  
Haoran Yan ◽  
Li Wang ◽  
Tiantian Zhang ◽  
Xiangting Jiang ◽  
Sensen Yang ◽  
...  

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Junmin Fang ◽  
Dechun Huang ◽  
Jingrong Xu

With the improvement of citizens’ risk perception ability and environmental protection awareness, social conflicts caused by environmental problems in large-scale construction projects are becoming more and more frequent. Traditional social risk prevention management has some defects in obtaining risk data, such as limited coverage, poor availability, and insufficient timeliness, which makes it impossible to realize effective early warning of social risks in the era of big data. This paper focuses on the three environments of diversification of stakeholders, risk media, and big data era. The evolution characteristics of the social risk of environmental damage of large-scale construction projects are analyzed from the four stages of incubation, outbreak, mitigation, and regression in essence. On this basis, a social risk early warning model is constructed, and the multicenter network governance mode of social risk of environmental damage in large-scale construction projects and practical social risk prevention strategies in different stages are put forward. Experiments show that the long short-term memory neural network model is effective and feasible for predicting the social risk trend of environmental damage of large-scale construction projects. Compared with other classical models, the long short-term memory model has the advantages of strong processing capability and high early warning accuracy for time-sensitive data and will have broad application prospects in the field of risk control research. By using the network governance framework and long short-term memory model, this paper studies the environmental mass events of large-scale construction projects on the risk early warning method, providing reference for the government to effectively prevent and control social risk of environmental damage of large-scale construction project in China.


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