Utilisation of Decision Support Systems in Construction Management: A Literature Survey

2020-11-12
Günay, Göksu
Pekeriçli, Mehmet Koray
Today, computers are widely used in all industries for data collection and analysis, interpretation of data and decision making processes. Especially Decision Support Systems, which are computer-based Information Systems providing tools for assisting managers in making semi-structured decisions, have gained stronger ground parallel to the latest developments in information technologies including artificial intelligence applications like fuzzy logic, machine learning, natural language processing, etc. There are on-going efforts to produce effective models, frameworks, and algorithms for decision making processes. However, construction industry has an intense volume of semi-structured or unstructured knowledge, and knowledge is mostly transferred as experiences from one project to another. This paper aims to find out how decisions are made in practice and how effective Decision Support Systems are in the construction industry.
6. International Project and Construction Management Conference (IPCMC)

Suggestions

Data science technology selection: development of a decision-making approach
Nazlıel, Kerem; Eren, Pekin Erhan; Kayabay, Kerem; Department of Information Systems (2022-12-29)
Developments in IT, Cloud, Analytics, and related fields have created an abundance of Data Science technologies for practitioners, developers, and organizations to use. This abundance and variety complicate the Data Science technology selection and management processes for the analytics teams. When teams select and use improper tools and technologies, they encounter problems and inefficiencies, also known as technical debt. As a remedy, this thesis proposes a systematic technology selection method consideri...
Using operational data for decision making a feasibility study in rail maintenance
Marsh, William; Nur, Khalid; Yet, Barbaros; Majumdar, Arnab (2016-05-01)
In many organisations, large databases are created as part of the business operation: the promise of ‘big data’ is to extract information from these databases to make smarter decisions. We explore the feasibility of this approach for better decision-making for maintenance, specifically for rail infrastructure. We argue that the data should be used within a Bayesian framework with the aim of inferring the underlying state of the system so we can predict future failures and improve decision-making. Within thi...
A hierarchical decision support system for workforce planning in medical equipment maintenance services
Cihangir, Çiğdem; Bayındır, Zeynep Pelin; Tan, Tarkan; Department of Industrial Engineering (2010)
In this thesis, we propose a hierarchical level decision support system for workforce planning in medical equipment maintenance services. In strategic level, customer clusters and the total number of field engineers is determined via a mixed integer programming and simulation. In MIP, we aim to find the minimum number of field engineers. Afterwards, we analyze service measures such as response time via simulation. In tactical level, quarterly training program for the field engineers is determined via mixed ...
A case study in weather pattern searching using a spatial data warehouse model
Köylü, Çağlar; Akyürek, Sevda Zuhal; Department of Geodetic and Geographical Information Technologies (2008)
Data warehousing and Online Analytical Processing (OLAP) technology has been used to access, visualize and analyze multidimensional, aggregated, and summarized data. Large part of data contains spatial components. Thus, these spatial components convey valuable information and must be included in exploration and analysis phases of a spatial decision support system (SDSS). On the other hand, Geographic Information Systems (GISs) provide a wide range of tools to analyze spatial phenomena and therefore must be ...
An application of the minimal spanning tree approach to the cluster stability problem
Volkovich, Z.; Barzily, Z.; Weber, Gerhard Wilhelm; Toledano-Kitai, D.; Avros, R. (Springer Science and Business Media LLC, 2012-03-01)
Among the areas of data and text mining which are employed today in OR, science, economy and technology, clustering theory serves as a preprocessing step in the data analyzing. An important component of clustering theory is determination of the true number of clusters. This problem has not been satisfactorily solved. In our paper, this problem is addressed by the cluster stability approach. For several possible numbers of clusters, we estimate the stability of the partitions obtained from clustering of samp...
Citation Formats
G. Günay and M. K. Pekeriçli, “Utilisation of Decision Support Systems in Construction Management: A Literature Survey,” İstanbul, Türkiye, 2020, p. 332, Accessed: 00, 2021. [Online]. Available: http://ipcmc2020.itu.edu.tr/conference/proceedings.