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1 – 10 of over 23000Ibrahim Yahaya Wuni and Khwaja Mateen Mazher
Modular integrated construction (MiC) is a modern construction method innovating and reinventing the traditional site-based construction method. As it integrates advanced…
Abstract
Purpose
Modular integrated construction (MiC) is a modern construction method innovating and reinventing the traditional site-based construction method. As it integrates advanced manufacturing principles and requires offsite production of volumetric building components, several factors and conditions must converge to make the MiC method suitable and efficient for building projects in each context. This paper aims to present a knowledge-based decision support system (KB-DSS) for assessing a project’s suitability for the MiC method.
Design/methodology/approach
The KB-DSS uses 21 significant suitability decision-making factors identified through literature review, consultation of experts and questionnaire surveys. It has a knowledge base, a DSS and a user interface. The knowledge base comprises IF-THEN production rules to compute the MiC suitability score with the efficient use of the powerful reasoning and explanation capabilities of DSS.
Findings
The tool receives the inputs of a decision-maker, computes the MiC suitability score for a given project and generates recommendations based on the score. Three real-world projects in Hong Kong are used to demonstrate the applicability of the tool for solving the MiC suitability assessment problem.
Originality/value
This study established the complex and competing significant conditions and factors determining the suitability of the MiC method for construction projects. It developed a unique tool combining the capabilities of expert systems and decision support system to address the complex problem of assessing the suitability of the MiC method for construction projects in a high-density metropolis.
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Daniel Yaw Addai Duah, Kevin Ford and Matt Syal
The purpose of this paper is to develop a knowledge elicitation strategy to elicit and compile home energy retrofit knowledge that can be incorporated into the development of an…
Abstract
Purpose
The purpose of this paper is to develop a knowledge elicitation strategy to elicit and compile home energy retrofit knowledge that can be incorporated into the development of an intelligent decision support system to help increase the uptake of home energy retrofits. Major problems accounting for low adoption rates despite well-established benefits are: lack of information or information in unsuitable and usable format for decision making by homeowners. Despite the important role of expert knowledge in developing such systems, its elicitation has been fraught with challenges.
Design/methodology/approach
Using extensive literature review and a Delphi-dominated data collection technique, the relevant knowledge of 19 industry experts, selected based on previously developed determinants of expert knowledge and suitable for decision making was elicited and compiled. Boolean logic was used to model and represent such knowledge for use as an intelligent decision support system.
Findings
A combination of comprehensive knowledge elicitor training, Delphi technique, semi-structured interview, and job shadowing is a good elicitation strategy. It encourages experts to describe their knowledge in a natural way, relate to specific problems, and reduces bias. Relevant and consensus-based expert knowledge can be incorporated into the development of an intelligent decision support system.
Research limitations/implications
The consensus-based and relevant expert knowledge can assist homeowners with decision making and industry practitioners and academia with corroboration and enhancement of existing knowledge. The strategy contributes to solving the knowledge elicitation challenge.
Originality/value
No previous study regarding a knowledge elicitation strategy for developing an intelligent decision support system for the energy retrofit industry exists.
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Marcin Lukasz Bartosiak and Artur Modlinski
The importance of artificial intelligence in human resource management has grown substantially. Previous literature discusses the advantages of AI implementation at a workplace…
Abstract
Purpose
The importance of artificial intelligence in human resource management has grown substantially. Previous literature discusses the advantages of AI implementation at a workplace and its various consequences, often hostile, for employees. However, there is little empirical research on the topic. The authors address this gap by studying if individuals oppose biased algorithm recommendations regarding disciplinary actions in an organisation.
Design/methodology/approach
The authors conducted an exploratory experiment in which the authors evaluated 76 subjects over a set of 5 scenarios in which a biased algorithm gave strict recommendations regarding disciplinary actions at a workplace.
Findings
The authors’ results suggest that biased suggestions from intelligent agents can influence individuals who make disciplinary decisions.
Social implications
The authors’ results contribute to the ongoing debate on applying AI solutions to HR problems. The authors demonstrate that biased algorithms may substantially change how employees are treated and show that human conformity towards intelligent decision support systems is broader than expected.
Originality/value
The authors’ paper is among the first to show that people may accept recommendations that provoke moral dilemmas, bring adverse outcomes, or harm employees. The authors introduce the problem of “algorithmic conformism” and discuss its consequences for HRM.
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This paper reports a web‐based intelligent system, called WebStra, for the formulation of marketing strategies and associated e‐commerce strategies. In the paper, the architecture…
Abstract
This paper reports a web‐based intelligent system, called WebStra, for the formulation of marketing strategies and associated e‐commerce strategies. In the paper, the architecture and functional components of the WebStra system are described. The system's effectiveness and efficiency are also evaluated. WebStra can be applied to support real‐world strategic marketing decision making. It may also be used as a useful tool for training and consultancy purposes.
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B.J. Garner, C.L. Forrester and D. Lukose
The concept of a knowledge interface for library users is developed as an extension of intelligent knowledge‐base system (IKBS) concepts. Contemporary directions in intelligent…
Abstract
The concept of a knowledge interface for library users is developed as an extension of intelligent knowledge‐base system (IKBS) concepts. Contemporary directions in intelligent decision support, particularly in the role of search intermediaries, are then examined to identify the significance of intelligent intermediaries as a solution to unstructured decision support requirements of library users. A DISCOURSE SCRIPT is given to illustrate one form of intelligent intermediary.
Yuliana Kaneu Teniwut, Marimin Marimin and Nastiti Siswi Indrasti
The purpose of this paper is to develop a spatial intelligent decision support system (SIDSS) for increasing productivity in the rubber agroindustry by green productivity (GP…
Abstract
Purpose
The purpose of this paper is to develop a spatial intelligent decision support system (SIDSS) for increasing productivity in the rubber agroindustry by green productivity (GP) approach. The SIDSS was used to measure the productivity of rubber plantation and rubber agroindustry by GP approach, and select the best strategies for increasing the productivity of rubber agroindustry.
Design/methodology/approach
This system was developed by combining spatial analysis, GP, and fuzzy analytic network process (ANP) with the model-based management system, which is able to provide comprehensive and meaningful decision alternatives for the development of natural rubber agroindustry. Rubber plantation productivity measurement model was used to find the productivity level of rubber plantation with fuzzy logic, and also to provide information and decision alternatives to all stakeholders regarding spatial condition of rubber agroindustry, production process flow, and analysis of the seven green wastes at each production process flow using the geographic information system. GP measurement model was used to determine the productivity performance of the rubber agroindustry with the green productivity index (GPI). The best strategy for increasing the productivity was determined with fuzzy ANP.
Findings
Rubber plantation measurement model showed that the average of plantation productivity was 6.25 kg/ha/day. GP measurement model showed that the GPI value of ribbed smoked sheet (RSS) was 0.730, whereas of crumb rubber (CR) was 0.126. The best strategy for increasing the productivity of rubber agroindustry was raw material characteristics control. Based on the best strategy, the GPI value of RSS was 1.340, whereas of CR was 0.228.
Research limitations/implications
This decision support system is still limited as it is based on static data; it needs further development so that it can be more dynamically based on developments in the rubber agroindustry related levels of productivity and environmental impact. In addition, details regarding the decision to increase the productivity of the rubber section by benchmarking efforts should be studied further, both among plantation as well as among countries such as Thailand so that the productivity of rubber plantation and agroindustry can be integrated.
Practical implications
This research can help the planters to select superior clones for rubber trees, to improve the technique of tapping latex, and to use a better coagulant. The good quality and quantity of raw material is a key factor in increasing the productivity of rubber agroindustry; if the quality of latex is good then the resulting product will also have a good quality and production cost can be reduced. In addition, the application of GP through the calculation of GPI value using improvement scenarios can be used as a reference and comparison for evaluating the performance of rubber agroindustry to reduce the waste generated by the activities of rubber processing plant.
Social implications
Reduction of waste generated by production activities can improve the quality of life of the workforce and the environment. The calculation of GPI value can also be used as a basis to use raw materials, water, and electricity more efficiently.
Originality/value
This system was developed by combining spatial analysis, GP, and fuzzy ANP with the model-based management system, which is able to provide comprehensive and meaningful decision alternatives for the development of natural rubber agroindustry.
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Yanlan Mei, Ping Gui, Xianfeng Luo, Benbu Liang, Liuliu Fu and Xianrong Zheng
The purpose of this paper is to take advantage of Internet of Things (IoT) for intelligent route programming of crowd emergency evacuation in metro station. It is a novel approach…
Abstract
Purpose
The purpose of this paper is to take advantage of Internet of Things (IoT) for intelligent route programming of crowd emergency evacuation in metro station. It is a novel approach to ensure the crowd safety and reduce the casualties in the emergency context. An evacuation route programming model is constructed to select a suitable evacuation route and support the emergency decision maker of metro station.
Design/methodology/approach
The IoT technology is employed to collect and screen information, and to construct an expert decision model to support the metro station manager to make decision. As a feasible way to solve the multiple criteria decision-making problem, an improved multi-attributive border approximation area comparison (MABAC) approach is introduced.
Findings
The case study indicates that the model provides valuable suggestions for evacuation route programming and offers practical support for the design of an evacuation route guidance system. Moreover, IoT plays an important role in the process of intelligent route programming of crowd emergency evacuation in metro station. A library has similar structure and crowd characteristics of a metro station, thus the intelligent route programming approach can be applied to the library crowd evacuation.
Originality/value
The highlights of this paper are listed as followings: the accuracy and accessibility of the metro station’s real-time information are improved by integrating IoT technology with the intelligent route programming of crowd emergency evacuation. An improved MABAC approach is introduced to the expert support model. It promotes the applicability and reliability of decision making for emergency evacuation route selection in metro station. It is a novel way to combine the decision-making methods with practice.
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The purpose of this paper is to raise awareness among manufacturing researchers and practitioners of the potential of Bayesian networks (BNs) to enhance decision making in those…
Abstract
Purpose
The purpose of this paper is to raise awareness among manufacturing researchers and practitioners of the potential of Bayesian networks (BNs) to enhance decision making in those parts of the manufacturing domain where uncertainty is a key characteristic. In doing so, the paper describes the development of an intelligent decision support system (DSS) to help operators in Motorola to diagnose and correct faults during the process of product system testing.
Design/methodology/approach
The intelligent (DSS) combines BNs and an intelligent user interface to produce multi‐media advice for operators.
Findings
Surveys show that the system is effective in considerably reducing fault correction times for most operators and most fault types and in helping inexperienced operators to approach the performance levels of experienced operators.
Originality/value
Such efficiency improvements are of obvious value in manufacturing. In this particular case, additional benefit was derived when the product testing facility was moved from the UK to China as the system was able to help the new operators to get close to the historical performance level of experienced operators.
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Fentahun Moges Kasie, Glen Bright and Anthony Walker
This paper aims to propose a theoretical decision support framework, which integrates artificial intelligence (AI), discrete-event simulation (DES) and database management…
Abstract
Purpose
This paper aims to propose a theoretical decision support framework, which integrates artificial intelligence (AI), discrete-event simulation (DES) and database management technologies so as to determine the steady state flow of items (e.g. fixtures, jigs, tools, etc.) in manufacturing.
Design/methodology/approach
The existing literature was carefully reviewed to address the state of the arts in decision support systems (DSS), the shortcomings of pure simulation-based and pure AI-based DSS. A conceptual example is illustrated to show the integrated application of AI, simulation and database components of the proposed DSS framework.
Findings
Recent DSS studies have revealed the limitations of pure simulation-based and pure AI-based DSS. A new DSS framework is required in manufacturing to address these limitations, taking into account the problems of flowing items.
Research limitations/implications
The theoretical DSS framework is proposed using simple rules and equations. This implies that it is not complex for software development and implementation. Practical data are not presented in this paper. A real DSS will be developed using the proposed theoretical framework and realistic results will be presented in the near future.
Originality/value
The proposed theoretical framework reveals how the integrated components of DSS can work together in manufacturing in order to determine the stable flow of items in a specific production period. Especially, the integrated performance of case-based reasoning (CBR) and DES is conceptually illustrated.
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Manish Gupta, B. Chandra and M.P. Gupta
– The purpose of this paper is to introduce architecture of an Intelligent Decision Support System to fulfill the emerging responsibilities of law enforcement agencies.
Abstract
Purpose
The purpose of this paper is to introduce architecture of an Intelligent Decision Support System to fulfill the emerging responsibilities of law enforcement agencies.
Design/methodology/approach
The proposed Intelligent Police System (IPS) is designed to meet the emerging requirements and provide information at all levels of decision making by introducing a multi-level structure of user interface and crime analysis model. The proposed framework of IPS is based on data mining and performance measurement techniques to extract useful information like crime hot spots, predict crime trends and rank police administration units on the basis of crime prevention measures.
Findings
IPS has been implemented on actual Indian crime data provided by National Crime Records Bureau (NCRB), which illustrates effectiveness and usefulness of the proposed system. IPS can play a vital role in improving outcome in the crime investigation, criminal detection and other major areas of functioning of police organization by analyzing the crime data and sharing of the information.
Research limitations/implications
The research in intelligent police information system can be enhanced with some important additional features which include web-base management system, geographical information system, mobile adhoc network technology, etc.
Practical implications
IPS can easily be applied to any police system in the world and can equally be useful for any law enforcement agencies for carrying out homeland security effectively.
Originality/value
The research reported in this manuscript is outcome of the research project funded by NCRB. This paper is the first attempt to build framework of IPS for Indian police who deal with large volume and high rate of crimes that are unmatched to any police force of the world.
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