Inductive consistency in knowledge-based decision support systems

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.

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.

A prototype of knowledge-based decision support system for construction cost management

Parents' and nonparents' perceptions of cries

A strategy for determining the optimal domain for knowledge based decision support systems

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.

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.

A tool that will allow spread sheet users and knowledge-based decision support systems (KB-DSS) professional designers to implement more advanced applications of the KB-DSS type than are possible with spreadsheet technology is described. This tool is meant to aid designers in increasing their productivity when they are required to develop the application. The KB-DSS design task is supported by an integrated development environment. Interface design and modeling are subtasks of KB-DSS design. >

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.

Construction sites are dynamic and complex; they are the most vulnerable environments to natural disasters such as hurricanes. Wind-caused damage to the construction sites could cause millions of dollar losses, considerable schedule delays, and threats to both worker safety and public safety. Therefore, there is sorely a need to enhance the overall resilience of construction projects and implement resilient construction operations. However, there is a lack of research that focuses on enhancing the resilience of construction operations during the construction phase as most existing research focuses on enhancing the structural resilience of the completed buildings or infrastructures. There is a lack of holistic and systematic knowledge that ensures whole life-cycle disaster management of construction projects. To address the gap, this paper aims to develop an ontology-based semantic model of disaster resilient construction operations to formally represent and reason about the knowledge of resilient construction operations during wind disasters. This paper starts by presenting the research method, and it follows by presenting our initial modeling and validation efforts towards a formal semantic model on disaster resilient construction operations. Finally, the paper discusses our proposed framework for a knowledge-based decision support system that allows for knowledge access, classification, and transfer on disaster resilient construction operations. The model, together with its potential application of the knowledge-based system would allow construction professionals to share and transfer domain-specific knowledge on construction disaster resilience, which will eventually contribute to enhance the whole life-cycle disaster resilience of construction projects.

A knowledge-based decision support system to improve sow farm productivity

Knowledge-based decision support systems (KBDSS) have evolved greatly over the last few decades. The key technologies underpinning the development of KBDSS can be classified into three categories: technologies for knowledge modelling and representation, technologies for reasoning and inference and web-based technologies. In the meantime, service systems have emerged and become increasingly important to value adding activities in the current knowledge economy. This paper provides a review on the recent advances in the three types of technologies, as well as the main application domains of KBDSS as service systems. Based on the examination of literature, future research directions are recommended for the development of KBDSS in general and in particular to support decision-making in service industry.

Development and evaluation of a knowledge-based system for traffic congestion management and control

A process knowledge representation approach for decision support in design of complex engineered systems

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.