Intelligent Decision Support System for Leadership Analysis

Gudauskas, Renaldas; Jokubauskiene, Saule; Zavadskas, Edmundas Kazimieras; Kaklauskas, Artūras; Binkyte, A.; Peciure, Lina; Budryte, Loreta; Prialgauskas, Darius

doi:10.1016/j.proeng.2015.10.022

Cited by 4 publications

(2 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… Publications examining project performance and success estimation (10%) are generally targeted toward project management, the majority focusing on decision support for the project manager, or the discipline and process of project management itself (Hajdasz, 2015;Gudauskas et al, 2015;Hanna et al, 2018;Mahfouz et al, 2018;Vickranth et al, 2019). Other studies focus on predicting and optimizing project performance, time, and cost (Mirahadi and Zayed, 2016;Jaber et al, 2019) or project evaluation (Erzaij et al, 2020).…”

Section: Conceptual 40%mentioning

confidence: 99%

Artificial Intelligence in Construction Projects: A Systematic Scoping Review

2022

EPPM-Journal

View full text Add to dashboard Cite

The use of artificial intelligence (AI) in construction projects has surged in recent years and is believed to represent a significant potential for increasing productivity and efficiency in the industry. The purpose of this paper is to present a state-of-the-art view of the field by conducting a review of publications concerning the topic of AI in construction and comparing the findings to previously conducted reviews. This paper provides an overview of the recent and current uses of AI in construction projects, through a descriptive analysis of the characteristics and contents of 86 peer-reviewed articles from 2015 to 2020. Although the application of AI in the industry is not entirely new, construction appears to currently be behind other industries in terms of adopting and adapting to AI. The results show that a wide range of research is conducted on AI in construction projects. A limited number of publication channels and authors stand behind a significant part of the reviewed publications. Most studies are conceptual or use a mixed-methods research design. The research addresses several areas of application, but there is a predominance of quantitatively based subfields of construction, such as estimation and cost control, logistics, planning, and scheduling. Future research should focus on developing holistic and process-oriented frameworks for projects to move from ambition to practice. Findings can inform the future development and implementation of AI in the construction industry context. For researchers, this study identifies areas in need of further attention and examines possibilities for future exploration of multidisciplinary approaches that combine construction engineering, project management, AI, and social science. For practitioners, the study highlights current trends and work within the field, providing an overview of the potential for pilot studies, tests, and innovations.

show abstract

Section: Conceptual 40%mentioning

confidence: 99%

Artificial Intelligence in Construction Projects: A Systematic Scoping Review

2022

EPPM-Journal

View full text Add to dashboard Cite

show abstract

“…Secondly, the objectivity of the choice is subject to the diversity of criteria thus the selection transforms into multiple criteria task. For instance, (as to the practical experience of the last 20 years including observations and findings of the research in question) when managing the selection process there can be up to 30 various criteria used, consisting of quantitative and qualitative ones (Hurson and Siskos 2014, Gudauskas et al 2015). For thorough, multifaceted assessment and synthesis under this circumstance sophisticate the selection task in a methodical sense.…”

Section: Improving Management Of the Selection Processmentioning

confidence: 99%

Managing Selection of Wind Power Generation Technologies

Tamošiūnas

2018

Business: Theory and Practice

View full text Add to dashboard Cite

The article presents the principled model for managing the selection of wind power generation technologies enabling business organizations to transform rationally their fossil fuel-based business models towards greater renewable energy reliance. The model is aimed at complex improvement of management of the evaluation, selection processes for public and private organizations keen on switching their business models towards greater use of wind power (including (or) other renewable energy-based technologies). The set of measures proposed has a pivotal focus on the economic utility of the latter with respect to balanced and sustained strategic development of business concerned. Accordingly, the model involves tools for solving the following tasks: setting up an evaluation unit revealing critical factors for rational execution of this task; contributing to situation analysis when determining wind power generation options and assessment criteria. In this respect, besides recommendations on managing data collection, the paper also provides a spectrum of criteria for measuring the attractiveness of wind power generation technologies in terms of economic utility. The latter allow to evaluate, compare possible options in a comprehensive and complex manner; improving assessment and selection task involving and rationally utilizing multi-criteria decision analysis measures including possibilities for combination of MCDA tools if needed. In the context of empirical investigations of the evolution of wind power generation technologies in the EU and globally over the last decade, the paper reveals the benefit of the use of the proposed model specifying all its phases to relevant techniques and actions. Results of its application in practice also confirm the prevailing flexibility when adjusting the model to the specifics of activities of public and private organizations as well as of economic sectors at state, county, and municipal levels.

show abstract