Hierarchical Selection Decision Support Problems in Conceptual Design

Bascaran, Eduardo; Bannerot, Richard; Mistree, Farrokh

doi:10.1080/03052158908941212

Cited by 57 publications

(14 citation statements)

References 10 publications

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“…Based on this decision-based perspective, many hierarchical decomposition methods have been proposed and tested in limited situations. Examples include the hierarchical selection decision support problem (DSP) for conceptual design [10], and the coupled compromised decision support problem proposed to solve hierarchical problems in structural design [1,2,[11][12][13].…”

Section: Frame Of Referencementioning

confidence: 99%

Ontology-Based Representation of Design Decision Hierarchies

Ming

Wang

Yan

et al. 2017

Journal of Computing and Information Science in Engineering

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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.

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Section: Frame Of Referencementioning

confidence: 99%

Ontology-Based Representation of Design Decision Hierarchies

Ming

Wang

Yan

et al. 2017

Journal of Computing and Information Science in Engineering

Self Cite

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“…Through this feature, the users will be able to quantitatively specify the characteristics based on which the concepts should be evaluated. The underlying construct used for implementing the decision mechanism is the (utility-based) selection Decision Support Problem (sDSP), as reviewed in the literature [6,20] and summarized in words in Table 1. In this context, designers are encouraged to specify their preferences, for example through the definition of scales [6] or multi-attribute utility functions [32], along with the solution-neutral problem description.…”

Section: Collective Concept Selectionmentioning

confidence: 99%

Towards a Function-Based Collective Innovation Framework

Messer

Grotepaß

Frenzel

et al. 2009

Volume 2: 29th Computers and Information in Engineering Conference, Parts a and B

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In this paper, we present a work-in-progress web-based framework to enable collective innovation via a combination of top-down structural and bottom-up self-organized processes in global enterprises. Problem: In current organizations, expertise is usually locked in discipline-specific project teams or departments based on existing product portfolios which restricts collective innovation through distributed networks of peers translating into increased innovation. Innovation projects are managed in stage gate processes using tools (such as proprietary project workspaces or product data management) that limit access to solutions on various levels of maturity/abstraction throughout the enterprise. Approach: Our approach to facilitate collective innovation in the early stages of product development involves identification and implementation of the following collective innovation mechanisms a) collective concept creation, b) collective concept selection, and c) collective information management. These innovation mechanisms are being instantiated in a web-enabled COllective INnovation (COIN) framework to synthesize collaborative bottom-up and structured top-down approaches fostering innovation. The COIN framework is thus based on self-organized collective innovation as well as function-based systematic conceptual design approaches thereby embodying both collaborative bottom-up and structured top-down structured aspects. From the proposed approach to collective innovation through innovation mechanisms and web enabled tools for implementing collaborative bottom-up and structured top-down structured aspects, global enterprises can benefit from the COIN-framework in fostering synergetic R&D-collaborations, know-how transfer and technology scouting during the early stages of product development. The value to global enterprises can further be significantly increased through application-tailored subspaces consisting of a collection of entities, loosely related by user-defined information links (e.g., tags), as exemplified for a sealing subspace and corkscrew design example in this paper.

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“…Hierarchical Decisions-involve interaction between other decisions, e.g., selection-selection, (Bascaran, et al, 1989;Shupe, et al, 1987).…”

Section: )mentioning

confidence: 99%

“…The coupled and possibly hierarchical selection and compromise decision support problems t h a t arise when a system is partially or entirely designed using available assets can be formulated and solved on the software package DSIDES. An example of this type of problem and its solution is given by Bascaran, et al, 1989. We have now briefly presented our framework, or guidelines, for a method of design using available assets. Recall t h a t we contend t h a t Living Systems Theory is ideally suited to designing by using available assets; we will now justify this assertion by illustrating how a feasible system configuration can be identified through an example of the design of an aircraft evacuation system.…”

Section: General Guidelines For Designing By Using Available Assetsmentioning

confidence: 99%

A method of design using available assets: Identifying a feasible system configuration

et al. 1994

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In the early stages of the product realization process, if the functional requirements of a system to be designed can be partitioned and represented, concepts can be quickly explored to meet these functions. If methods and tools for partitioning and representing functional requirements can be developed, it becomes poesible to arrange functional components rapidly to represent specific concepts, allowing designen to explore different system configurations at a high level of abstraction. Likewise, if component or component assemblies performing certain functions are known to exist, a designer can determine the feasibility of realizing the product specific functions for each feasible system contiguration using available parts. It is also possible that a product may often be realized through partial use of available assets while part of the system is designed and manufactured.

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Hierarchical Selection Decision Support Problems in Conceptual Design

Cited by 57 publications

References 10 publications

Ontology-Based Representation of Design Decision Hierarchies

Ontology-Based Representation of Design Decision Hierarchies

Towards a Function-Based Collective Innovation Framework

A method of design using available assets: Identifying a feasible system configuration

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