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There are a number of decision-making problems encountered by a building design team. This issue is apparent in assessment of building envelope materials and designs in the early design stage. The purpose of this paper is to develope a decision support tool based on a quality function deployment (QFD) approach integrated with a knowledge management system (KMS) and fuzzy theory to facilitate a building design team to simultaneously mitigate the decision-making problems when assessing the building envelope materials and designs for the first instance.

This study engaged a design team comprising three decision makers (DMs) to test the developed decision support tool through a case study of a representative building project. The study employed deductive qualitative data analysis with use of a framework analysis approach to analyze perspectives of the DMs after completing the case study through a semi-structured interview.

A mapping diagram derived qualitatively from the framework analysis suggested that the tool can help mitigate the identified decision-making problems as a whole.

Practical contributions of using the decision support tool include achievement of a more efficient design and construction management, and higher productivity of a project. In terms of academic contributions, this study expands capabilities of a conventional decision support system, KMS, and QFD tool to handle decision-making problems.

When introducing new health technologies, decision makers must integrate research evidence with local operational management information to guide decisions about whether and under what conditions the technology will be used. Multi‐criteria decision analysis can support the adoption or prioritization of health interventions by using criteria to explicitly articulate the health organization's needs, limitations, and values in addition to evaluating evidence for safety and effectiveness. This paper seeks to describe the development of a framework to create agreed‐upon criteria and decision tools to enhance a pre‐existing local health technology assessment (HTA) decision support program.

The authors compiled a list of published criteria from the literature, consulted with experts to refine the criteria list, and used a modified Delphi process with a group of key stakeholders to review, modify, and validate each criterion. In a workshop setting, the criteria were used to create decision tools.

A set of user‐validated criteria for new health technology evaluation and adoption was developed and integrated into the local HTA decision support program. Technology evaluation and decision guideline tools were created using these criteria to ensure that the decision process is systematic, consistent, and transparent.

This framework can be used by others to develop decision‐making criteria and tools to enhance similar technology adoption programs.

The development of clear, user‐validated criteria for evaluating new technologies adds a critical element to improve decision‐making on technology adoption, and the decision tools ensure consistency, transparency, and real‐world relevance.

The purpose of this paper is to design a system deployment model that integrates case-based agent technique to develop an eco-responsibility decision support tool for greening educational institutions toward environmental responsibility.

Data were collected through questionnaires distributed among a statistical population that comprised practitioners across educational institutions in Malaysia that implement green practices. The questionnaire measured the feasibility of the developed tool based on factors derived from the literature. Accordingly, descriptive, exploratory and factor analysis approach using statistical package for social sciences (SPSS) was used to test the feasibility of the developed tool.

Results from descriptive analysis confirm the tool is feasible based on mean values that range from 4.1619 to 3.6508 on a five-point scale, indicating that the tool is effective in sustaining educational institutions going green. Besides, results from exploratory analysis verify the reliability of the tool based on the acceptable Cronbach’s alpha reliability coefficient score higher than 0.7 and Kaiser–Meyer–Olkin value being above 0.5. Finally, results from factor analysis reveal that the developed tool is usable, efficient, helpful, flexible and credible and supports educational institutions in going green at 88.44 per cent of the total variance, suggesting that the respondents are satisfied with the tool.

The sample population in this study comprises only practitioners from educational institutions in Malaysia. Theoretically, this research provides feasibility factors and associated items that can be used in evaluating developed information systems.

Practically, this study develops an eco-responsibility decision support tool to facilitate green strategies and provides information on how practitioners in educational institutions can improve green growth.

This study presents how case-oriented agents aid educational institutions in going green for environmental responsibility. Socially, this research provides the strategies for green practice improvement in educational institutions toward environmental responsibility.

The eco-responsibility decision support tool provides a Web-based platform for promoting ecological protection by supporting the measuring of practitioners’ current green practices for environmental responsibility. Thus, research findings from this study are expected to help decision-makers generate useful insights into environment-friendly strategies to be implemented in educational institutions. Lastly, the statistical tests adopted in this paper can be used to gauge the feasibility of information system application in future.

The purpose of this paper is to identify key project delivery method selection factors to assist water industry decision-makers in selecting the most appropriate delivery method for their water treatment plant projects.

The selection factors were identified by compiling and validating key project delivery selection factors across various industries through an extensive literature review and two industry expert workshops. This resulted in the development of a web-based decision-support tool to facilitate project delivery method selection within the water industry.

The research effort led to the identification of 13 key project delivery method selection factors (seven primary factors and six secondary factors) for water treatment plant projects. These factors were utilized to develop EXPRSS-TP, a pioneering web-based project delivery method decision-support tool for the water industry.

A project delivery method selection process is typically an informal process that may range from days to weeks at a time. Based on this work, the assessment can now be completed in about one hour and provides decision-makers with the most favorable delivery method for their project. And with the new tool that encompasses the new knowledge, not only is the decision reached at an accelerated pace, EXPRSS-TP also documents the entire selection process, allowing for a written and retained record of this key decision and its procedure.

This paper contributes to the exisiting body of knowledge by identifying key project delivery selection factors across numerous industries, assessing and combining them, and finally incorporating them into one comprehensive process. EXPRSS-TP improves the traditional project delivery method selection process and provides evidence-based project delivery method selection recommendations.

Today, industrial revolutions demands advanced technologies, means, mediums, tactics and so forth for optimizing their operating behavior and opportunities. It is probed that the effectual results can be seized into system by not only developing advance means and technologies, but also capably adapting these developed technologies, their user interface and their utilization at optimum levels. Today, industrial resources need perfect synchronization and optimization for getting elevated results. Accordingly, present study is furnished with the purpose to expose quality-driven insights to march toward excellence by optimizing existing resources by the industrial organizations. The present study evaluates quality attributes of mechanical machineries for seizing performance opportunities and maintaining competitiveness via synchronizing and reconfiguring firm's resources under quality management system.

In the present study, Kano’s integrated approach is implemented for supporting decision rational concerning industrial assets. The integrative Kano–analytic hierarchy process (AHP) approach is used to reflect the relative importance of quality attributes. Kano and AHP tactics are integrated to define global relative weight and their computational medium is adapted along with ratio analysis, reference point theory and TOPSIS technique for understanding robust decision. The study described an interesting idea for underpinning quality attributes for benchmarking system substitutes. A machine tool selection case is discussed to disclose the significant aspect of decision-making and its virtual qualities.

The decision executives can realize massive benefits by streaming quality data, advanced information, technological advancements, optimum analysis and by identifying quality measures and disruptions for gaining performance deeds. The study determined quality measures for benchmarking machine tool substitute for industrial applications. Momentous machine alternatives are evaluated by means of technical structure, dominance theory and comparative analysis for supporting decision-making of industrial assets based on optimization and synchronization.

The study linked financial, managerial and production resources under sole platform to present a technical structure that may assist in improving the performance of the manufacturing firms. The study provides a decision support mechanism to assist in reviewing the momentous resources to imitate a higher level of productive strength toward the manufacturing firms. The study endeavors its importance toward optimizing resources, which is an evident requirement in industries as the same not only saves money, escalates production, improves profit margins and so forth, but also gratifies the consumption of scarce natural resources.

The study stressed that advance information can be sought from system characteristics in the form of quality measures and attributes, which can be molded for gaining elevated outcomes from existing system characteristics. The same demands decision supports tools and frameworks to utilize data-driven information for benchmarking operations and supply chain activities. The study portrayed an approach for ease of utilizing data-driven information by the decision-makers for demonstrating superior outcomes. The study originally conceptualized multi-attributes appraisement framework associated with subjective cum objective quality measures to evaluate the most significant machine tool choice amongst preferred alternatives.

An intelligent decision‐support system was designed for assessing schedulability prior to assigning parts for scheduling. This was designed as part of a larger system for intelligent, real‐time control of a manufacturing system operation, where one of the system objectives was just‐in‐time delivery of production output. The manufacturing system was a conventional cellular manufacturing system where machines were assumed occasionally to fail. All necessary requirements for the processing of a job should actually or anticipatively be satisfied before a job is scheduled. The decision‐support system attempts to satisfy these requirements. The system thus helps the decision maker to make the right decision when system and customer constraints are violated. Illustrates the operation of the system through an example.

The purpose of this paper is to develop a knowledge‐based decision support tool used for assisting en‐route air traffic controllers by generating resolutions for dual aircraft conflicts after being integrated into a model‐based conflict detection and conflict resolution system.

Air traffic controller knowledge, which was obtained from the literature research, about solving aircraft conflicts is represented in a decision tree. Then it is written in Visual Basic programming language. With reference to several rules form the expert air traffic controller knowledge and some factors which affect controller conflict resolution process, the tool generates advisories consisting of eight kinds of separation strategies.

The paper finds that it is expected to increase the safety of air traffic system by supporting air traffic controller in conflict resolution process. Controller workload can be reduced by fast, reliable and acceptable resolution advisories of the tool.

The accuracy of decision tree is limited with the adequacy and quality of knowledge obtained from references, several assumptions and interpretation. Because of the unavailability of a model‐based conflict detection and resolution tool, the tool could not be evaluated in simulations.

After being integrated into a model‐based decision support tool, it can reduce the deficiencies of the model‐based tool such as low degree of resolution acceptance by controllers and low‐resolution speed by providing expert air traffic controller knowledge to the tool.

Additive manufacturing (AM) can reduce the process supply chain and encourage manufacturing innovation in remote or austere environments by producing an array of replacement/spare parts from a single raw material source. The wide variety of AM technologies, materials, and potential use cases necessitates decision support that addresses the diverse considerations of deployable manufacturing. The paper aims to discuss these issues.

Semi-structured interviews with potential users are conducted in order to establish a general deployable AM framework. This framework then forms the basis for a decision support tool to help users determine appropriate machines and materials for their desired deployable context.

User constraints are separated into process, machine, part, material, environmental, and logistical categories to form a deployable AM framework. These inform a “tiered funnel” selection tool, where each stage requires increased user knowledge of AM and the deployable context. The tool can help users narrow a database of candidate machines and materials to those appropriate for their deployable context.

Future work will focus on expanding the environments covered by the decision support tool and expanding the user needs pool to incorporate private sector users and users less familiar with AM processes.

The framework in this paper can influence the growth of existing deployable manufacturing endeavors (e.g. Rapid Equipping Force Expeditionary Lab – Mobile, Army’s Mobile Parts Hospital, etc.) and considerations for future deployable AM systems.

This work represents novel research to develop both a framework for deployable AM and a user-driven decision support tool to select a process and material for the deployable context.

The purpose of this paper is to identify the most appropriate multidisciplinary data sources related with energy optimization decision support as well as the related methodologies, tools and techniques for data capturing and processing for each of them.

A review is conducted on the state-of-play of decision support systems for energy optimization, focussing on the municipal sector, followed by an identification of the most appropriate multidisciplinary data sources related with energy optimization decision support. An innovative methodology is outlined to integrate semantically modeled data from multiple sources, to assist city authorities in energy management.

City authorities need to lead relevant actions toward energy-efficient neighborhoods. Although there are more and more energy and other related data available at the city level, there are no established methods and tools integrating and analyzing them in a smart way, with the purpose to support the decision-making process on energy use optimization.

A novel multidimensional approach is proposed, using semantic technologies to integrate data from multiple sources, to assist city authorities to produce short-term energy plans in an integrated, transparent and comprehensive way.

With the rising concern of safety, health and environmental performance, eco-labeled product and service are becoming more and more popular. However, the long and complex process of eco-labeling sometimes demotivates manufacturers and service providers to be certificated. The purpose of this paper is to propose a decision support platform aiming at further improvement and acceleration of the eco-labeling process in order to democratize a broader application and certification of eco-labels, also to consolidate the credibility and validity of eco-labels.

This decision support platform is based on a comprehensive knowledge base composed of various domain ontologies that are constructed according to an official eco-label criteria documentation.

Through standard Resource Description Framework and Web Ontology Language ontology query interface, the assets of the decision support platform will stimulate domain knowledge sharing and can be applied into other applications. A case study of laundry detergent eco-labeling process is also presented in this paper.

The authors present a reasoning methodology based on inference with Semantic Web Rule Language (SWRL) rules which allows decision making with explanation.