Research on construction schedule risk management of power supply and distribution projects based on MCS-AHP model

Author:

Xinfa Tang,Tian Zhong,Xingwu Huang,Dan Li

Abstract

In order to manage the construction schedule risk of power supply and distribution engineering, a construction schedule risk evaluation model, namely the Monte Carlo simulation method - Analytic Hierarchy Process (MCS-AHP) model, is proposed. In this model, the Monte Carlo simulation method is adopted to improve the analytic Hierarchy Process (AHP), and the normal distribution interval is used to replace the specific value when constructing the fuzzy complementary judgment matrix, to reduce the risk of fuzzy thinking and incomplete information or scattered data in the process of investigation and judgment and improve the scientific evaluation. This paper takes a power supply and distribution project in Guangdong Province as an example uses the MCS-AHP model to measure the key factors limiting the project progress, and uses the AHP method for comparative analysis, to verify the feasibility of the MCS-AHP model. The analysis shows that the key influencing factors are material and equipment procurement, production and arrival, installation of 10 kv high voltage switchboard, electrical acceptance and single machine commissioning, installation of low-voltage switchboard and DC switchboard, and foundation construction of power station equipment, etc., which are consistent with the actual situation. Therefore, it is feasible to construct the MCS-AHP model, which can provide a new way of thinking for schedule risk management analysis.

Publisher

Frontiers Media SA

Subject

Economics and Econometrics,Energy Engineering and Power Technology,Fuel Technology,Renewable Energy, Sustainability and the Environment

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Risk Management Of The Startup Projects;2023 IEEE 4th KhPI Week on Advanced Technology (KhPIWeek);2023-10-02

2. Research on the promotion of digital teaching and learning toward achieving China’s dual-carbon strategy;Frontiers in Environmental Science;2023-01-24

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