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Abstract The automation and intelligence highlighted in Industry 4.0 put forward higher requirements for reasonable trade-offs between humans and machines for decision-making governance. However, in the context of Industry 4.0, the vision of decision support for design engineering is still unclear. Additionally, the corresponding methods and system architectures are lacking to support the realization of value-chain-centric complex engineered systems design lifecycles. Hence, we identify decision support demands for complex engineered systems designs in the Industry 4.0 era, representing the integrated design problems at various stages of the product value chain. As a response, in this paper, the architecture of a Knowledge-Based Design Guidance System (KBDGS) for cloud-based decision support (CBDS) is presented that highlights the integrated management of complexity, uncertainty, and knowledge in designing decision workflows, as well as systematic design guidance to find satisfying solutions with the iterative process “formulation-refinement-exploration-improvement” (FREI). The KBDGS facilitates diverse multi-stakeholder collaborative decisions in end-to-end cloud services. Finally, two design case studies are conducted to illustrate the proposed work and the efficacy of the developed KBDGS. The contribution of this paper is to provide design guidance to facilitate knowledge discovery, capturing, and reuse in the context of decision-centric digital design, thus improving the efficiency and effectiveness of decision-making, as well as the evolution of decision support in the field of design engineering for the age of Industry 4.0 innovation paradigm.
41A process knowledge representation approach for decision support in design of complex engineered systems
A process knowledge representation approach for decision support in design of complex engineered systems
A prototype of knowledge-based decision support system for construction cost management
A prototype of knowledge-based decision support system for construction cost management
8Inductive consistency in knowledge-based decision support systems
Inductive consistency in knowledge-based decision support systems
13Developing a knowledge-based multi-objective decision support system
Decision support systems under multiple objectives (MODSS) has been classified as a specific type of system within the broad family of DSS and has been one of the most active areas of research. However, despite its theoretical development large scale real-world applications of multiobjective decision-making (MODM) methods had been seriously lacking due to the technical knowledge and expertise needed to select and apply the most appropriate method. This paper explores the possibility of embedding intelligent guidance within MODSS, and outlines a specific guidance framework for the design of knowledge-based guidance for the selection of a suitable MODM method. This framework has been implemented as an intelligent and graphical user interface based multiple objective decision support system prototype. The prototype, with its database, methodology base of MODM and knowledge based system, will help any decision-maker in selecting the most appropriate method in solving various MODM problems.
Read more4RFID-Embedded Decision Support for Tracking Surgical Equipment
Healthcare and supply chain management have recently been the two most active areas for RFID applications. The healthcare environment is a natural fit for generating and utilizing instance-level data for decision support. We consider a scenario from the French healthcare environment involving tracking and tracing of surgical equipment within and among hospitals and develop a knowledge-based system for decision support that helps improve the overall performance of the surgical instrument management process while reducing errors. We illustrate the process through the developed healthcare knowledge-based system and evaluate its performance.
Read more52Applied Decision Support with Soft Computing
I: General Issues.- Modeling Knowledge: Model-based Decision Support and Soft Computations.- Benefits of Decision Support Using Soft Computing.- Evolving Connectionist-based Decision Support Systems.- An Agent-based Soft Computing Society with Application Applications in Financial Investment Planning.- A Rough Sets/Neoral Networks Approach to Knowledge Discovery for the Development of Decision Support Systems.- II: Applications and Implementations.- Decision Support Systems in Healthcare: Emerging Trends and Success Factors.- A View of Public and Private Sectors for Taiwan's BOT Project Financing Using Fuzzy Multi-Criteria Methods.- Relational Structures for the Analysis of Decision Information in an Electronic Market.- A Fuzzy Evaluation Model: A Case for Intermodal Terminals in Europe.- Application of Kemeny's Median for Group Decision Support.- An Internet-based Group Decision and Consensus Reaching Support System.- Limpio: A DSS for the Urban Waste Collection Problem.- A Decision Support System for Air Quality Control Based on Soft Computing Methods.- Multicriteria Genetic Tuning for the Optimization and Control of HVAC Systems.- Intelligent Information Systems for Crime Analysis.- Application of Fuzzy Decision Trees to Reservoir Recognition.- Introducing SACRA: A Decision Support System for the Construction of Cattle Diets.- Prediction of Parthenium Weed Dispersal Using Fuzzy Logic on GIS Spatial Image.
Read moreParents' and nonparents' perceptions of cries
Parents' and nonparents' perceptions of cries
111ARIADNE: A knowledge-based interactive system for planning and decision support
The development of an interactive planning and decision support process for multiple-criteria alternative selection situations is discussed. Probabilities, utility scores for the lowest level attributes, and attribute tradeoff weights, i.e. the parameters, can be imprecisely described by set inclusion. Within a specified structural model of the decision situation, the process allows the decision-maker to iteratively select the mix of parameter value precision and alternative ranking specificity. By selecting this mix, the decision-maker is able to direct the alternative selection process in an iterative manner, using alternative selection strategies based on behaviorally meaningful dominance search strategies.
Read more3Research on key technologies of knowledge-based engineering decision support system for circular economy
This paper combines with the characteristic of knowledge-based engineering decision problem for circular economy, analyses knowledge representation, rule representation, reasoning mechanism and knowledge base structure of multi-objective attribute decision problems. It proposes a software architecture of knowledge-based engineering decision support system, which lays the foundation for the realization of the dynamic monitoring of the basic conditions of regional sustainable development and accurately reflecting on the relationship of the various elements and the evolution of the process.
Read more7A strategy for determining the optimal domain for knowledge based decision support systems
A strategy for determining the optimal domain for knowledge based decision support systems
2Normative or Confirmative: Effects of Information Quality and Redundancy in Decision Support Systems
ObjectiveThe study investigates users’ tendency to access decision support (DS) systems as a function of the correlation between the DS information and the information users already have, the ongoing interaction with such systems, and the effect of correlated information on subjective trust.BackgroundPrevious research has shown inconclusive findings regarding whether people prefer information that correlates with information they already have. Some studies conclude that individuals recognize the value of noncorrelated information, given its unique content, while others suggest that users favor correlated information as it aligns with existing evidence. The impact of the level of correlation on performance, subjective trust, and the decision to use DS remains unclear.MethodIn an experiment (N = 481), participants made classification decisions based on available information. They could also purchase additional DS with different degrees of correlation with the available information.ResultsParticipants tended to purchase information more often when the DS was not correlated with the available information. Correlated information reduced performance, and the effect of correlation on subjective trust and performance depended on DS sensitivity.ConclusionAdditional information may not improve performance when it is correlated with available information (i.e., it is redundant). Hence, the benefits of additional information and DS depend on the information the system and the operator use.ApplicationIt is essential to analyze the correlations between information sources and design the available information to allow optimal task performance and possibly minimize redundancy (e.g., by locating sensors in different positions to capture independent data).
Read moreDecision-Maker Modeling for Decision Support Application
Product development decisions represent challenging processes with various stakeholders from different disciplines and with different perspectives involved. Decisions can be highly complex due to the high amount of influencing factors that have to be considered simultaneously. Among other methods and tools, decision support systems (DSS) were developed to support decision-makers in handling complex decisions. One main disadvantage of DSS is the little adaptation to and integration of decision-maker needs and characteristics. Thus, current trends focus on the development of human- and decision-maker-centered DSS. This paper aims in providing a multilevel stepwise procedure for the modeling of decision-makers in order to enable a more effective decision support. The decision-maker modeling procedure is based on findings from literature research and results from research projects performed in cooperation with industry partners. The procedure serves as a guideline for generating diverse decision-maker models for different applications and for integrating human cognitive thinking procedures within the design and functionality of decision support methodologies and systems. The procedure represents an important design element of the human-centered decision support methodology currently being developed at the Institute of Product Development of the Technische Universität München (Germany).
Read more46Development and evaluation of a knowledge-based system for traffic congestion management and control
Development and evaluation of a knowledge-based system for traffic congestion management and control
A Hybrid Inference Framework for Model Selection
A hybrid inference framework is presented in which the aim is to select decision models through natural language in knowledge-based decision support system. In situations in which the rules that determine a system are uncertain and incomplete, the knowledge elicitation, maintenance and update of the rule-based system can be the problematic tasks. In such a situation, it has been found that a hybrid case-based and rule-based reasoning framework can provide a more effective and accurate means of performing such selection than other statistical methods and models. The hybrid framework results in capturing knowledge in context by storage of the case that prompted a new rule to be added, which aims at the unwanted side effects associated with typical rule reasoning system. This framework has been used to determine the inventory models in the decision support system. The results obtained from the experiment are presented with the both efficiency-improving and accuracy-improving.
Read moreIn Memoriam: Dr. Clive L. Dym (1942–2016)
In Memoriam: Dr. Clive L. Dym (1942–2016)
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