Evolutionary fuzzy hybrid neural network for dynamic project success assessment in construction industry

Cheng, Min‐Yuan; Tsai, Hsing‐Chih; Sudjono, Erick

doi:10.1016/j.autcon.2011.05.011

Cited by 34 publications

(23 citation statements)

References 16 publications

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“…Over the past decade, the Neural Networking (NN) has become one of the most popular optimization and event simulation methods in a number of engineering fields. In construction industry, it is mostly used for simulation of events and resources as an alternative or addition to the well-known Monte-Carlo simulation of events [30][31][32][33][34][35]. Hola and Schabowicz have made NN models for estimating the time and cost of serial operation of soil excavation machines.…”

Section: Optimization Methods For Final Selection Machinerymentioning

confidence: 99%

Scenarijski simulacijski model za optimalnu alokaciju građevinskih strojeva

2016

JCE

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Optimization methods for narrowing down the choice of resources, and for their allocation, are highly important in all phases of construction projects. This particularly concerns complex project environments where resources (construction machines) have to be allocated to several construction projects at different phases and with different priorities. The authors propose a model based on the multicriteria optimisation algorithm and the ranking of scenarios involving sub-optimal programs for serial multi-channel operation of construction machines, with a fixed number of machines for final selection, and with separation of the optimum program and/or program scenario cross-sections in complex environment. Scenarijski simulacijski model za optimalnu alokaciju građevinskih strojeva Metode optimizacije imaju veliku važnost pri sužavanju izbora resursa na konačni i pri alokaciji resursa u svim fazama projekata. To se naročito odnosi na složena projektna okruženja gdje je potrebno provesti alokaciju resursa (strojeva) na više građevinskih projekata u njihovim raznim fazama i prioritetima. U radu se predlaže model koji se zasniva na algoritmu višekriterijske optimizacije i rangiranja scenarija suboptimalnih programa serijskog višekanalnog rada građevinskih strojeva pri konačnom broju strojeva za izbor, te izlučivanje presjeka optimalnih programa i/ili scenarija programa u složenom okruženju.

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Section: Optimization Methods For Final Selection Machinerymentioning

confidence: 99%

Scenarijski simulacijski model za optimalnu alokaciju građevinskih strojeva

2016

JCE

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“…This time the selected tools are neural networks fused with high order neural networks fused with fuzzy logic and genetics algorithms creating a model named Evolutionary Fuzzy Hybrid Neural Network (EFHNN), integrated again with CAPP for dynamically identifying critical success factors. The main difference with EFNIM is the combined use of neural networks and high order neural networks, which allow greater flexibility and let us see how mapped inputs and outputs of the model really are [40].…”

Section: B Determining Project Successmentioning

confidence: 99%

“…Furthermore we find papers that try to forecast project success for the duration of the project life cycle in its early stage, or at any other time point of the project [38], [40], [42], [51]- [54]the project management, in which the final status of project is estimated, must be incorporated.In this paper, we consider estimation of the final status(that is, successful or unsuccessfulDuring the literature review, these algorithms applied to project success prediction have been found: Artificial Intelligence application for project success predicting is relatively recent, since the first reference is from 2006. This model estimates project final state applying a Bayesian classifier to different metrics collected from a project.…”

Section: B Determining Project Successmentioning

confidence: 99%

Artificial Intelligence Applied to Project Success: A Literature Review

Martínez¹,

Rodríguez²

2015

IJIMAI

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-Project control and monitoring tools are based on expert judgement and parametric tools. Projects are the means by which companies implement their strategies. However project success rates are still very low. This is a worrying situation that has a great economic impact so alternative tools for project success prediction must be proposed in order to estimate project success or identify critical factors of success. Some of these tools are based on Artificial Intelligence. In this paper we will carry out a literature review of those papers that use Artificial Intelligence as a tool for project success estimation or critical success factor identification.

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“…Literature Review Success and Failure in Research Project Management There exists broad divergence of opinions on the subject of what constitutes 'research project success.' Success, considered primarily as project success in general and not only in reference to research projects, refers traditionally to three basic criteria for successful projects in an 'iron triangle' or 'golden triangle': cost, time, and quality (Atkinson, 1999;Baccarini, 1999;Cheng, Tsai, & Sudjono, 2012). In general, the understanding of a successful project appears obvious and yet, project management literature reveals inconsistencies of an omnifarious nature.…”

Section: Introductionmentioning

confidence: 99%

Management of Research Projects – Lessons Learned

Betta

Jastrzębska

2017

CBUP

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Project management assists many areas of human activities and recently has been considered as a way to ensure the success of scientific research projects. A primary problem is the need to identify the project's success factors, which include phenomena, actions, events, and parameters that contribute to the success. The purpose of this study is to identify the management issues and oversights that pose a negative effect on scientific research projects. The study involves a questionnaire, distributed to several managers of research projects conducted within Polish universities. This paper presents the results of assessing responses to two questions of this survey. These questions about project management deal with goals achieved by marginal projects and an analysis ex post. The responses to each question are grouped into five types, on which a frequency analysis is performed. For each group, several conclusions and recommendations are proposed. The results are a first attempt to construct a set of suitable practices for the management of scientific research projects. JEL Classification IntroductionThere has been growing interest in project management of scientific research. The issue has remained neglected for many years, leaving the management of scientific research projects lagging behind that of other areas such as information and technology. Awareness is needed on the history of project management and the specifics of research projects since those requiring a high level of innovation and creativity may not fit within the rigid framework of algorithms and quantitative tools. The work conducted in this paper is empirical, based on the experiences of many scientists who implement and manage scientific projects, and results from the scientist's perceived need to change. The intention is to find the starting point for future development of project management research, or at least to identify a set of 'good practices' in this field. The aim of the work is to show the areas considered unfavorable for research projects where appropriate improvements could lead to eliminate the most common mistakes for better project management in the future. The target audience includes individuals managing scientific research projects, as well as other stakeholders.

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Evolutionary fuzzy hybrid neural network for dynamic project success assessment in construction industry

Cited by 34 publications

References 16 publications

Scenarijski simulacijski model za optimalnu alokaciju građevinskih strojeva

Scenarijski simulacijski model za optimalnu alokaciju građevinskih strojeva

Artificial Intelligence Applied to Project Success: A Literature Review

Management of Research Projects – Lessons Learned

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