Defining a beta distribution function for construction simulation

Fente, Javier; Knutson, Kraig; Schexnayder, Cliff

doi:10.1145/324898.324983

Cited by 23 publications

(13 citation statements)

References 9 publications

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“…For example, Schexnayder et al (2005) proposed that the beta distribution is the best suited for modeling construction durations. Other researches confirm this recommendation (AbouRizk et al 1991;Fente et al 1999). Beta distribution based on three points of data, mean, lowest and highest point is recommended for representing cost cumulative distribution (Wang 2002;Touran and Wiser 1992).…”

Section: System Frameworkmentioning

confidence: 92%

A Framework for Integrated Analysis of Building Designs Using a Life-Cycle Assessment and Energy Simulation

Inyim

Zhu

2013

Iccrem 2013

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The building and construction industry has been steadily adopting the concept of sustainable design. Consequently the design phase of a building project has become a critical stage in determining how sustainability goals of a project will be accomplished. Life cycle assessment (LCA) is a quantitative method to determine environment impacts of materials, products, processes or buildings. However, it is often a challenge to quantify use phase building energy consumption in life cycle assessment due to the dynamic nature of this phase. Energy consumption is an important factor in the life cycle of a building project. Many researches acknowledge that buildings are responsible for a significant amount of energy consumption in the USA and around the world country. There is currently an increasing demand from both public and private sectors to deliver buildings that are energy efficient. A feasible set of tools in dealing with the energy efficiency analysis during design are energy simulation programs, which allow the evaluation of energy performance of different building designs and the selection of the most appropriate alternatives. This research is focused on developing a framework for integrated analysis of building designs using life cycle assessment and energy simulation by extending the existing SimulEICon. Such a framework allows tracking design parameters such as energy consumption and costs during the entire design phase of a project. Construction and building life cycle related data are stored in an alternatives database, which includes the probability distributions of individual parameters and the possible combinations among such parameters. Monte Carlo simulation is used to address data uncertainty and availability. The energy simulation software, Energy Plus is used in order to calculate use phase energy consumption. Genetic algorithm optimization is applied to address the issue of selecting optimal solutions from a large number of possible design alternatives during the decision-making process at the design phase. A case study is presented to demonstrate the feasibility and potential of the framework.

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Section: System Frameworkmentioning

confidence: 92%

A Framework for Integrated Analysis of Building Designs Using a Life-Cycle Assessment and Energy Simulation

Inyim

Zhu

2013

Iccrem 2013

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“…In the absence of detailed survey data, f-Distribution [4] has been used to establish estimates when reasonable guesses on range for specific variables can be made. We utilized :-distribution to establish the WTP function, using the IDC report spending ranges, for various ILECS in the NAR market.…”

Section: Market Segmentation Sizingmentioning

confidence: 99%

Economic Comparison of Delivery Platforms for Converged Sevices

Aguilar¹,

Bass²,

Prakash³

et al. 2006

Networks 2006. 12th International Telecommunications Network Strategy and Planning Symposium

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As markets and technology undergo rapid change and ARPUs decline operators are looking for new ways to generate revenue and retain existing customers. In this environment, converged services present a valuable alternative to commodity-priced voice and data services. These applications are designed to enhance a customer's quality of life by offering greater convenience, efficiency, and expanded entertainment options. To benefit from the offering of such services, operators need to find the most efficient platform for new service introduction. In this analysis we compare deployment via IMS versus traditional Point Solution.The economic analysis shows that, with IMS, a collection of convergence applications can be delivered significantly sooner than a traditional point solution. This crucial time-to-market advantage is then quantified in terms of higher market penetration and enhanced revenues, while reducing churn in a competitive marketplace. The results provide compelling evidence that operators gain an opportunity to capture and keep "early adopters" and the high-end, technology-savvy market segments, which are willing to pay a premium for value-added services. In addition, IMS platform enables service blending that enables service differentiation and stickiness resulting in customer retention.

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“…In this, each activity duration has to be defined as a random variable which follows a certain theoretical distribution. With this purpose, we use the beta PERT distribution (J. Fente, K. Knutson, C. Schexnayder [3], P.A. Jensen [4]).…”

Section: Introductionmentioning

confidence: 99%

Implementation of computer-aided statistical analysis in a simulation scheduling model

Đukić¹,

Dukić²,

Sesar

2008

ITI 2008 - 30th International Conference on Information Technology Interfaces

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A range of methods have been developed as project management support. In order to improve decision-making efficiency in conditions of uncertainty, simulations began to be applied in the area of project management. In performing such simulations, the durations of project activities have to be defined as random variables. In this paper it is assumed that activity durations follow a beta PERT distribution. Different information required for decisionmaking in such a simulation model is obtained by means of adequate statistical methods. The decision-making process can be further improved by modified usage of certain elements of statistical analysis. A computer-aided statistical analysis thus becomes an indispensable tool for a project manager. The construction of adequate decision-making system and the development of software applications are prerequisites for implementing the proposed model.

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Defining a beta distribution function for construction simulation

Cited by 23 publications

References 9 publications

A Framework for Integrated Analysis of Building Designs Using a Life-Cycle Assessment and Energy Simulation

A Framework for Integrated Analysis of Building Designs Using a Life-Cycle Assessment and Energy Simulation

Economic Comparison of Delivery Platforms for Converged Sevices

Implementation of computer-aided statistical analysis in a simulation scheduling model

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