Techniques for Intelligent Decision Support Systems
01 Jan 2016-pp 159-183
TL;DR: This chapter will look into knowledge-based systems and genetic algorithms which are both widely intelligent systems applied in the construction industry and both are complex techniques that can optimize and find solutions for problems which standard optimization techniques fail to solve.
Abstract: Techniques for systems that support intelligent decision making are the new way of applying what is called artificial intelligence, which can assist in solving complex problems in program management. They embody human-like techniques to solve problems ranging from planning, scheduling, and optimization to expert decision making that are difficult to solve using standard mathematical modeling, as described in previous chapters. This chapter will look into knowledge-based systems (also called expert systems) and genetic algorithms which are both widely intelligent systems applied in the construction industry. Knowledge-based systems use computer programming to solve problems associated with human reasoning. They are much simpler than other artificial intelligence methods and can be used effectively in program management where many decisions need to be made, and where the logic can be structured and developed into a software program. Knowledge-based systems are described, together with their applications in the construction industry and especially from the viewpoint of program management. These are empty programs which users can apply to solve their unique problems, thus freeing the hand of the user from the programming. Genetic algorithms, however, are complex techniques that can optimize and find solutions for problems which standard optimization techniques fail to solve. They are search algorithms that mimic the way evolution has progressed by creating a never-ending supply of better generations.
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TL;DR: The proposed study presents a decision support system to deal with big data and scientific programming for the Industrial Internet of Things and has used the tool of SuperDecisions to plot the hierarchy of situations and select the best alternative among the available.
Abstract: Big data is a challenging issue as its volume, shape, and size need to be modified in order to extract important information for a specific purpose. The amount of data is rising with the passage of time. This increase in volume can be a challenging issue to analyze the data for smooth industry and the Internet of things. Several tools, techniques, and mechanisms are available to support the handling and management process of such data. Decision support systems can be one of the important techniques which can support big data in order to make decisions on time. The proposed study presents a decision support system to deal with big data and scientific programming for the Industrial Internet of Things. The study has used the tool of SuperDecisions to plot the hierarchy of situations of big data and scientific programming and to select the best alternative among the available.
11 citations
26 Nov 2018
TL;DR: In this paper, the authors employed an extant review of the literature, cross-section survey of construction managers of building projects and experts interview in the Middle East to identify and evaluate the influencing of the key performance criteria on selecting construction methods for building projects.
Abstract: There is a lack of an efficient systematic approach to the selection of appropriate construction methods for building projects. Not only various innovative methods are now available, but also established methods may often be adapted inappropriately, without recourse to the necessary scientific foundation of their efficiency. The result is that there is a low level of performance on building projects. This study examines how key performance criteria were used in the selection of construction methods on projects. The study employed an extant review of the literature, cross-section survey of construction managers of building projects and experts interview in the Middle East to identify and evaluate the influencing of the key performance criteria on selecting construction methods for building projects. It emerged from the Pearson Correlation Coefficient and Analytical Hierarchy Process analysis that key performance criteria consisting of time, quality, and cost have strong positive significant roles in the selection of construction methods used on building projects and that these selection criteria differed depending on the building components. The study concludes that the likelihood of a construction method being selected for use on projects in the Middle East depends on its ability to shorten the duration, improving the quality and reduce the cost of projects.
7 citations
References
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01 Jan 1995
TL;DR: The expert systems are the offshoots of artificial intelligence which is concerned with using computers to simulate human intelligence in a limited way and the key issue behind all these developments is the knowledge acquisition, knowledge representation and knowledge processing.
Abstract: In the last decade or so, expert systems also called knowledge-based systems have made their way from research laboratories into the real world. Applications have been, and are continuing to be, developed in areas as diverse as business, medicine, manufacturing, defence, astronomy, science and engineering. Such applications perform tasks that include interpretation, prediction, diagnosis, design, planning, monitoring, debugging, repairing, instruction and control. The expert systems are the offshoots of artificial intelligence which is concerned with using computers to simulate human intelligence in a limited way. Some researchers define artificial intelligence (AI) as the science of making machines do things that would require intelligence if done by men. In the last few decades, AI has spread into major subfields including expert systems, artificial neural networks, fuzzy systems, evolutionary computation and chaos theory. Some researchers do not differentiate between expert systems and knowledge-based systems. The key issue behind all these developments is the knowledge acquisition, knowledge representation and knowledge processing.<>
28 citations
01 Jan 2003
TL;DR: In this article, a methodology termed "multi-constraint scheduling" is introduced, in which four major groups of construction constraints including physical, contract, resource, and information constraints are considered.
Abstract: SUMMARY Reliable construction schedule is vital for effective co-ordination across supply chains and various trades at construction work face. According to the lean construction concept, reliability of the schedule can be enhanced through detection and satisfaction of all potential constraints prior to releasing operation assignments. However, it is difficult to implement this concept since current scheduling tools and techniques are fragmented and designed to deal with a limited set of construction constraints. This paper introduces a methodology termed ‘multi-constraint scheduling’ in which four major groups of construction constraints including physical, contract, resource, and information constraints are considered. A Genetic Algorithm (GA) has been developed and used for multi-constraint optimisation problem. Given multiple constraints such as activity dependency, limited working area, and resource and information readiness, the GA alters tasks’ priorities and construction methods so as to arrive at optimum or near optimum set of project duration, cost, and smooth resource profiles. This feature has been practically developed as an embedded macro in MS Project. Several experiments confirmed that GA can provide near optimum solutions within acceptable searching time (i.e. 5 minutes for 1.92E11 alternatives). Possible improvements to this research are further suggested in the paper.
20 citations
TL;DR: A newly devised approach that employs genetic algorithms (GAs) for the optimization of material delivery schedules and their associated inventory control is presented in this article, based on the project material requirement plans, and employs an objective function that minimizes the total costs associated with material deliveries.
Abstract: Purpose – To develop a systematic procedure and a computerized tool for optimizing the delivery and inventory of materials, as part of a comprehensive material management system in construction projectsDesign/methodology/approach – A newly devised approach that employs genetic algorithms (GAs) for the optimization of material delivery schedules and their associated inventory control is presented The approach is based on the project material requirement plans, and employs an objective function that minimizes the total costs associated with material deliveries Furthermore, the computer system developed is used to examine and validate the adopted approachFindings – GA proved to be a satisfactory approach for optimizing material delivery schedules and its associated inventory levels The selected case study particularly showed the system to produce material delivery plans that have reduced costs compared with their actual counterparts Also, the computer processing time for developing the optimized plans
18 citations
Proceedings Article•
10 May 2006TL;DR: The KBDSS would assist project managers by providing accurate and timely information for decision-making, and a user-friendly system for analyzing and selecting the controls for variation orders for institutional buildings.
Abstract: This study describes the framework for developing a knowledge-based decision support system (KBDSS) for making more informed decisions for managing variation orders in institutional buildings. The KBDSS framework consists of two main components, i.e., a knowledge base and a decision support shell. The database will be developed through collecting data from source documents of 80 institutional projects, questionnaire survey, literature review and in-depth interview sessions with the professionals who were involved in these institutional projects. The knowledge base will be developed through initial sieving and organization of data from the database. The decision support shell would provide decision support through a structured process consisting of building the hierarchy between the main criteria and the suggested controls, rating the controls, and analyzing the controls for selection through multiple analytical techniques. The KBDSS would be capable of displaying variations and their relevant details, a variety of filtered knowledge, and various analyses of available knowledge. This would eventually lead the decision maker to the suggested controls for variations and assist in selecting the most appropriate controls. The KBDSS would assist project managers by providing accurate and timely information for decision-making, and a user-friendly system for analyzing and selecting the controls for variation orders for institutional buildings. The study would assist building professionals in developing an effective variation management system. The system would be helpful for them to take proactive measures for reducing variation orders. The findings from this study would also be valuable for all building professionals in general.
12 citations
TL;DR: The KBS enables the user to selectively modify and constrain single activities or the entire network by specified amounts, reconfigure the network, and recompute resource usage in each case and represents a potentially useful decision support tool for performing exploratory and sensitivity analyses in managing large projects and a training tool for less-experienced planners.
10 citations