Decision Support System for Roof Installation
Daiva MAKUTĖNIENĖ1, Olga Regina ŠOSTAK1, Augustinas MACEIKA2
1 Department of Engineering Graphics, Vilnius Gediminas Technical University,
11, Saulėtekio al. 609 room, LT–10223 Vilnius, Lithuania
2 Department of Mechanical Engineering, Vilnius Gediminas Technical University,
28, J. Basanavičiaus Street, 1012 room, LT-03224 Vilnius, Lithuania
Abstract: The authors of the article carried out a feasibility study of the complex decision support system (DSS) which is designed for a successful roof installation. Analysis was made to examine the created database (DB) of the roof design variants. Delphi technique was used to determine the main parameters and sub-parameters for possible roofing projects variants evaluation and to assess importance and weight of the parameters. After assessment of the complex DSSs creation methodology, parameters used in the system, indicators and the practical aspects of the application, it was planned to create the prototype of the system. For this purpose DSSs of a variety of application fields and their development processes were researched. Based on the research results, the authors proposed the complex DSS model, which enables customers to explore different roof installation projects and inform them about the benefits and costs of the projects being assessed.
Keywords: Decision support system, decision making, roof installation, case study, Delphi method.
CITE THIS PAPER AS:
Daiva MAKUTĖNIENĖ, Olga Regina ŠOSTAK, Augustinas MACEIKA, Decision Support System for Roof Installation, Studies in Informatics and Control, ISSN 1220-1766, vol. 25(2), pp. 163-172, 2016. https://doi.org/10.24846/v25i2y201603
Application of the complex DSS for roofing activities creates new opportunities to make a more effective decision and to choose the most suitable option, which would align the interests of the customer, architect, and builder.
Key indicators influencing the choice of roofing are balance between quality and cost, design requirements, and roof construction techniques. The most common mistake, in selecting roofing, is decisions after evaluation only of the roofing price per square meter. Indeed, first of all it is worth to count all the construction or renovation costs if one or another roof covering was chosen, only then to compare. Here you need to evaluate everything from the price, which includes the specific elements of the roof structure, roofing with all the necessary accessories and ending with a price of the roofing. Assessing the quality of the roof, durability, and aesthetics, it is also worth to look into the manufacturer’s experience guarantee.
According to the authors, after the assessment of the conflicts resolution complexity, the number of participants, the abundance of information and data, in order to perform a successful roofing work it is necessary to have advanced DSS. The system’s objective and purpose is to collect, analyse, and visualize the data and processes, after they are submitted for the experts and building customer assessment. Importance of the information visualization was defined in scientific publications of many scientists and there are potential benefits from using visual representations of project interdependence (C.P. Killen (2013)). The system collected various types of data – numerical and textual, graphical and logical. Their search and management can be optimized by means of imaging techniques.
According to the T. L., Saaty and H. S. Shih (2009) geometry is necessary to represent the structure for decision making too. They proposed to use new kind of subjective geometry: a graph of a hierarchy or a network, as such a representation makes it easier to visualize and understand the relevant issues and their interactions and enables us to solve the problem with greater efficiency, relevance and confidence.
To improve the roofing projects evaluation by the experts the Delphi method was selected. According to A. Marchais-Roubelat, F. Roubelat (2011), the Delphi method is important to give access to specific forms of knowledge and this knowledge may be characterized according to the type of knowledge sought after, its status, its temporality, and its field of use and the risk of bias that may affect it. The aim is to obtain advice regarding the action to be made within the scope of an aid to decision-making.
The goal of this work is to make a feasibility study of the complex DSS for roofing modelling. For this purpose DSSs of a variety of application fields were researched and their development tendencies were analysed. Based on the analysis, the authors proposed the prototype model of the complex DSS.
Applied methods – analysis of scientific literature and other information sources, study of the DSSs variants, analysis of its application cases, Delphi approach to establish parameters for evaluation, and the modelling of complex DSS for the roofing.
According to F.G. Filip et al. (2014), the steadily increasing interest in the research area of DSS was identified during the period 2010-2013 and a lot of examples of DSS development methodology was presented in the scientific publications.
X. Luo et al. (2011) concluded that the group DSS can effectively facilitate the implementation of value management in construction briefing. The system allows a client to define and represent his or her requirements with functions and functional performance, to bring forward ideas to achieve the functions, and finally to evaluate and highlight the ideas against the functional performance for further development in design.
D. C. Novak, C.T. Ragsdale (2003) proposed methodology for solving stochastic, multi-criteria linear programming problems. J. Gottschlich, O. Hinz (2014) proposed DSS that can support the investor in three different aspects. First, by creating a ranked list, the investor gets advice about the most preferable securities on a specific date. In addition, the system supports the implementation and simulation of strategies based on the computed ranking so that investors can test and explore different approaches to identify promising investment strategies. Once a suitable strategy has been identified, the system can be used to automatically follow a specified strategy day by day and create orders to modify a portfolio. The system’s task is to transform crowd votes into actionable share ratings for a given day.
In K. Fagerholt et al. (2010) methodology Microsoft Excel is used for input of case and scenario information to Turbo Router and for output of results from the analysis.
A. Kengpol, P. Neungrit (2014) proposed methodology consists of the prediction modelling and risk assessment analysis. G. Kyriakarakos et al. (2014) methodology approach followed for the implementation of decision support toolkit consists of four discrete stages: parameters (legal/regulative/administrative, financial, technical, social and environmental) investigation, indicators choice, fuzzy cognitive maps implementation, and implementation of the decision support toolkit in a web-platform.
According to L. Yu, K.K. Lai (2011) multi-person multi-criteria group decision making model is composed of six main procedures: to construct the group decision making environment, to select different decision criteria for decision alternative evaluation, to formulate various decision alternatives, to use criteria weight determination methods to determine criteria weights, to give different decision results for every alternative, to aggregate different decision results into a group consensus in terms of the maximum agreement principle. The aggregated group consensus value can be used as a final measurement for the final decision-making purpose.
According to G. Desanctis and R.B. Gallupe (1987), three environmental contingencies are identified as critical to of group decision support systems design: group size, member proximity, and the task confronting the group.
According to the T. Wanderer, S. Herle (2015) decision making is influenced by a multitude of different physical, economic or social criteria with many of them being of spatial nature.
C. Bolchini et al. (2007) proposes a design methodology for very small DB. According methodology the main mobility issues are considered along with data distribution, context awareness is included in the data design issues to allow full exploitation of context-sensitive application functionalities, and the peculiarities of the storage device are taken into account by introducing a logistic phase.
R. Mohemad et al. (2010) proposed a framework of ontological-based extraction for decision support system in order to improve tender assessment process. In order to automate the tendering processes, integrating ontology in DSS model seems to be a promising approach.
In D. Tang et al. (2011) paper, a novel method is proposed to generate a belief rule base, which is the basis of the Belief Rule-Base Inference Methodology using the Evidential Reasoning. Due to its capability in dealing with complex reasoning problems under uncertainty, Rule-Base Inference Methodology using the Evidential Reasoning is then applied to assess customer perception risk in a new product development process.
According to J. P. Shim et al. (2002), DSS support process can be enhanced by continued developments in Web-enabled tools, wireless protocols, and group support systems, which can expand the interactivity and pervasiveness of decision support technologies.
E. Dupuit et al. (2007) proposed methodology for decision support approach to manage a refinery wastewater treatment plant by using model of the network.
The consensus support system presented by S. Alonso et al. (2010) aims were to facilitate experts to express their preferences on the alternatives in the problem while maintaining their consistency and to provide easy to understand recommendations in form of simple rules to help them to converge to a solution for the problem with a high level of consensus.
In the H. Konu (2015) study, the Delphi was implemented in order to develop services by involving the customers in the process. Customer ideas and opinions are used in new product and service development even though it has sometimes been found challenging e.g. by criticising that customers do not necessarily know what they want. Two Delphi rounds were used. The first round was used to collect new ideas for different purposes in new service development and these ideas were then analysed and thematic products/product themes were formed by using narrative analysis. During the second round in the comments related to the thematic products, alternative forms or products were suggested.
According to the Đ.T.N. Quyên (2014) the Delhi method stages consist of the Pre-Delphi construction of potential indicators, panel selection and recruitment, data collection and analysis. Data collection and analysis was of three rounds. In Round 1, proposed set of indicators was discussed, in Round 2, experts were asked to rate the level of importance of the indicators using the scale from 0 to 4. Coefficient of Quartile Variation (CQV) was used to measure the level of consensus among ratings and Coefficient of variation (CV = σ/μ) was used to measure the extent to which indicators in a factor vary in weights, in Round 3, the interviews with experts were transcribed, and thematic analysis was applied to analyse the data.
After evaluation of above examined literature, the authors of the article proposed the following applied methods for complex DSS system modelling:
- Alternatives of roofs selection: analysis of all roofs types and the elements of the roofs.
- Project organization.
- Customers objects analysis: objects preserved by State; VIP roofs, the average customer orders, cheap and other options.
- Data Base formation.
- Establishment of the decision making criterions and system of the parameters creation by using Delphi method.
- Decision making.
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