Ontology-Based Representation of Design Decision Hierarchies

2016 ◽  
Author(s):  
Zhenjun Ming ◽  
Yan Yan ◽  
Guoxin Wang ◽  
Jitesh H. Panchal ◽  
Chung Hyun Goh ◽  
...  
Keyword(s):  
Decision Support ◽  
Engineering Design ◽  
Building Blocks ◽  
Design Decision ◽  
Information Flows ◽  
Knowledge Modeling ◽  
Frame Design ◽  
Complex Engineered Systems ◽  
Portal Frame ◽  
Engineered Systems

It is efficacious to capture and represent the knowledge for decision support in engineering design. Ontology is a promising knowledge modeling scheme in the engineering domain. In this paper, an ontology is proposed for capturing, representing and documenting the knowledge related to hierarchical decisions in the design of complex engineered systems. The ontology is developed based on the coupled Decision Support Problem (DSP) construct, taking into consideration the requirements for a computational model that represents a decision hierarchy. Key to the ontology is the concept of two classes, namely, Process which represents the basic hierarchy building blocks where the DSPs are embedded, and Interface which represents the DSP information flows that link different Processes to a hierarchy. The efficacy of the ontology is demonstrated using a portal frame design example.

scholarly journals Ontology-Based Representation of Design Decision Hierarchies

2017 ◽  
Vol 18 (1) ◽  
Author(s):  
Zhenjun Ming ◽  
Guoxin Wang ◽  
Yan Yan ◽  
Jitesh H. Panchal ◽  
Chung Hyun Goh ◽  
...  
Keyword(s):  
Building Blocks ◽  
Engineering System ◽  
Design Decision ◽  
Engineering Systems ◽  
Knowledge Modeling ◽  
Frame Design ◽  
Hierarchical Decision ◽  
Complex Engineering ◽  
Engineering System Design ◽  
Decision Based Design

The design of complex engineering systems requires that the problem is decomposed into subproblems of manageable size. From the perspective of decision-based design (DBD), typically this results in a set of hierarchical decisions. It is critically important for computational frameworks for engineering system design to be able to capture and document this hierarchical decision-making knowledge for reuse. Ontology is a formal knowledge modeling scheme that provides a means to structure engineering knowledge in a retrievable, computer-interpretable, and reusable manner. In our earlier work, we have created ontologies to represent individual design decisions (selection and compromise). Here, we extend the selection and compromise decision ontologies to an ontology for hierarchical decisions. This can be used to represent workflows with multiple decisions coupling together. The core of the proposed ontology includes the coupled decision support problem (DSP) construct, and two key classes, namely, Process that represents the basic hierarchy building blocks wherein the DSPs are embedded, and Interface to represent the DSP information flows that link different Processes to a hierarchy. The efficacy of the ontology is demonstrated using a portal frame design example. Advantages of this ontology are that it is decomposable and flexible enough to accommodate the dynamic evolution of a process along the design timeline.

Rave: A Computational Framework to Facilitate Research in Design Decision Support

2012 ◽  
Vol 12 (2) ◽  
Author(s):  
Matthew J. Daskilewicz ◽  
Brian J. German
Keyword(s):  
Decision Support ◽  
Wide Spectrum ◽  
Problem Formulation ◽  
Design Decision ◽  
Computational Framework ◽  
Complex Engineered Systems ◽  
Research Platform ◽  
Engineered Systems ◽  
Support Methods ◽  
Design Decision Support

The cognitive challenges in the design of complex engineered systems include the scale and scope of decision problems, nonlinearity of the trade space, subjectivity of the problem formulation, and the need for rapid decision making. These challenges have motivated an active area of research in design decision-support methods and the development of commercial and openly available design frameworks. Although these frameworks are extremely capable, most are limiting as a basis for research relating to design decision support because they offer little user flexibility for incorporating and evaluating new features or techniques. This paper describes Rave (www.rave.gatech.edu), a computational framework designed specifically as a research platform for design decision-support methods. Rave has been structured to be flexible and adaptable, handle data with systematic data structures and descriptive metadata, facilitate a wide spectrum of visualization types, provide features to enable user interactivity and linking of graphics, and incorporate surrogate modeling and optimization as enabling capabilities. This framework is envisioned to provide the research and industrial communities an easily expandable and customizable baseline capability to facilitate investigation of further design decision-support advancements.

Taxonomy for classifying engineering decision problems and support systems

1995 ◽  
Vol 9 (5) ◽  
pp. 427-438 ◽  
Author(s):  
David G. Ullman ◽  
Bruce D'Ambrosio
Keyword(s):  
Decision Support ◽  
Engineering Design ◽  
Quantitative Data ◽  
Decision Problems ◽  
Design Decision ◽  
Decision Support Tools ◽  
Design Decisions ◽  
Wide Range ◽  
Support Tools ◽  
Need Support

AbstractThe design of even the simplest product requires thousands of decisions. Yet few of these decisions are supported with methods on paper or on computers. Is this because engineering design decisions do not need support or is it because techniques have yet to be developed that are usable on a wide basis? In considering this question a wide range of decision problem characteristics need to be addressed. In engineering design some decisions are made by individuals, others by teams – some are about the product and others about the processes that support the product – some are based on complete, consistent, quantitative data and others on sparse, conflicting, qualitative discussions. To address the reasons why so little support is used and the characteristics of potentially useful decision support tools, a taxonomy of decision characteristics is proposed. This taxonomy is used to classify current techniques and to define the requirements for an ideal engineering design decision support system.

A Taxonomy for Classifying Engineering Decision Problems and Support Systems

1995 ◽  
Author(s):  
David G. Ullman ◽  
Bruce D’Ambrosio
Keyword(s):  
Decision Support ◽  
Engineering Design ◽  
Quantitative Data ◽  
Decision Problems ◽  
Design Decision ◽  
Decision Support Tools ◽  
Design Decisions ◽  
Wide Range ◽  
Support Tools ◽  
Need Support

Abstract The design of even the simplest product requires thousands of decisions. Yet very few of these decisions are supported with methods on paper or on computers. Is this because engineering design decisions don’t need support or is it because techniques have yet to be developed that are usable on a wide basis? In considering this question a wide range of decision problem characteristics need to be addressed. In engineering design some decisions are made by individuals, others by teams — some are about the product and others about the processes that support the product — some are based on complete, consistent, quantitative data and others on sparse, conflicting, qualitative discussions. In order to address the reasons why so little support is used and the characteristics of potentially useful decision support tools, a taxonomy of decision characteristics is proposed.1 This taxonomy is used to classify current techniques and to define the requirements for an ideal engineering design decision support system.

Knowledge-Based Design Guidance System for Cloud-Based Decision Support in the Design of Complex Engineered Systems

2021 ◽  
Vol 143 (7) ◽  
Author(s):  
Ru Wang ◽  
Jelena Milisavljevic-Syed ◽  
Lin Guo ◽  
Yu Huang ◽  
Guoxin Wang
Keyword(s):  
Decision Support ◽  
Value Chain ◽  
Industry 4.0 ◽  
Guidance System ◽  
Design Engineering ◽  
Knowledge Based ◽  
Complex Engineered Systems ◽  
Engineered Systems ◽  
Knowledge Based Design ◽  
Design Guidance

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.

Ontology-Based Representation of Meta-Design in Designing Decision Workflows

2017 ◽  
Author(s):  
Ru Wang ◽  
Guoxin Wang ◽  
Yan Yan ◽  
Maryam Sabeghi ◽  
Zhenjun Ming ◽  
...  
Keyword(s):  
Decision Making Process ◽  
Thermal System ◽  
Design Decision ◽  
Design Processes ◽  
Future Design ◽  
Complex Engineered Systems ◽  
Hierarchical Decision ◽  
Enterprise Agility ◽  
Engineered Systems ◽  
Product And Process

Utilizing the enterprise capital related the knowledge of design processes has become a crucial to improve enterprise agility and respond to shifts or changes in markets. The complexity and uncertainty of design processes raise the challenge of capturing tacit knowledge and the ability to provide assistance in designing design processes. In this paper, an ontology is proposed for capturing, representing and documenting the knowledge related to hierarchical decision workflows in the meta-design of complex engineered systems. The ontology is developed in the context of Decision Support Problem Technique (DSPT), taking into account the requirements being able to guide assistance in designing design workflows, and integrating problem, product and process information in a design decision-making process. Then, the method of building procedure and design of process templates are presented to facilitate the reuse of the populated template instances in future design. Finally, the meta-design of the heat exchanger in a small thermal system is presented as an example to illustrate the effectiveness of this approach.

Ontology-Based Representation of Meta-Design in Designing Decision Workflows

2018 ◽  
Vol 19 (1) ◽  
Author(s):  
Ru Wang ◽  
Guoxin Wang ◽  
Yan Yan ◽  
Maryam Sabeghi ◽  
Zhenjun Ming ◽  
...  
Keyword(s):  
Decision Making Process ◽  
Thermal System ◽  
Design Decision ◽  
Design Processes ◽  
Future Design ◽  
Complex Engineered Systems ◽  
Hierarchical Decision ◽  
Enterprise Agility ◽  
Engineered Systems ◽  
Product And Process

Utilizing the enterprise capital related the knowledge of design processes has become crucial to improve enterprise agility and respond to shifts or changes in markets. The complexity and uncertainty of design raise the challenge of capturing tacit knowledge and the ability to aid in designing design processes. In this paper, ontology is proposed for capturing, representing, and documenting the knowledge related to hierarchical decision workflows in the meta-design of complex engineered systems. The ontology is developed in the context of decision support problem technique (DSPT), considering the requirements being able to guide assistance in designing design workflows, and integrating problem, product, and process information in a design decision-making process. Then, the approach for building procedure of process templates is presented to facilitate the reuse of the populated template instances in future design. Finally, the meta-design of the heat exchanger in a small thermal system is presented as an example to illustrate the effectiveness of this approach.

Toward a Value-Driven Design Approach for Complex Engineered Systems Using Trade Space Exploration Tools

2014 ◽  
Author(s):  
Simon W. Miller ◽  
Timothy W. Simpson ◽  
Michael A. Yukish ◽  
Gary Stump ◽  
Bryan L. Mesmer ◽  
...  
Keyword(s):  
Data Visualization ◽  
Space Exploration ◽  
Systems Design ◽  
Design Decision ◽  
Value Functions ◽  
Complex Engineered Systems ◽  
A Value ◽  
Trade Offs ◽  
Engineered Systems ◽  
Trade Space Exploration

Design decision-making involves trade-offs between many design variables and attributes, which can be difficult to model and capture in complex engineered systems. To choose the best design, the decision-maker is often required to analyze many different combinations of these variables and attributes and process the information internally. Trade Space Exploration (TSE) tools, including interactive and multi-dimensional data visualization, can be used to aid in this process and provide designers with a means to make better decisions, particularly during the design of complex engineered systems. In this paper, we investigate the use of TSE tools to support decision-makers using a Value-Driven Design (VDD) approach for complex engineered systems. A VDD approach necessitates a rethinking of trade space exploration. In this paper, we investigate the different uses of trade space exploration in a VDD context. We map a traditional TSE process into a value-based trade environment to provide greater decision support to a design team during complex systems design. The research leverages existing TSE paradigms and multi-dimensional data visualization tools to identify optimal designs using a value function for a system. The feasibility of using these TSE tools to help formulate value functions is also explored. A satellite design example is used to demonstrate the differences between a VDD approach to design complex engineered systems and a multi-objective approach to capture the Pareto frontier. Ongoing and future work is also discussed.

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