A Taxonomy for Classifying Engineering Decision Problems and Support Systems

Author(s):  
David G. Ullman ◽  
Bruce D’Ambrosio

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.

Author(s):  
David G. Ullman ◽  
Bruce D'Ambrosio

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.


Author(s):  
Michael W Mehaffy

Urban design decision support tools aimed at achieving desired outcomes – such as reduction of greenhouse gas emissions – must respond to the inherent complexity of urban systems, and the inherent uncertainties within measurement and inventory methods. Moreover, they must accommodate the epistemological limitations of all models, arising from their dynamic relationship with the often self-modifying phenomena they are intended to model. Drawing on methodologies from other fields, we present here the outline of a methodology that meets that requirement, exploiting the capacity for iteration, empirical evaluation, and collaborative refinement over time. We show how this methodology is suitable for application in a new generation of decision support tools for urban design. 


2021 ◽  
Author(s):  
◽  
Michael Robert Donn

<p>The spur for this research was a lack of use by architecture practitioners of the environmental design decision support tools (eddst’s) they learn to use during their education. It was hypothesised that lessons for the improvement of eddst’s could be found in a systematic examination of the problems encountered by design teams with a range of currently available eddst’s. The research plan was to establish through surveys and case studies how practising architects who have tried to use building eddst’s assess the effectiveness of these tools. A range of different types of eddst was examined, each addressing a different aspect of the environment in buildings. The research did not achieve its original goal of developing a formula for the generation of new eddst’s for architects in the fields of building acoustics, lighting, thermal design and aerodynamics. What was found is a more fundamental common denominator underlying building design eddst’s: the need for built-in Quality Assurance measures that assure not only the architect, but also the simulationist and the client of the reality of the ebuilding performance predictions. It was found that contrary to their general reputation, designers do want detailed quantitative environmental information. They want to be able to discuss costs and benefits of decisions. However, they also want to be able to understand and trust this information. The output from eddst’s must therefore also be qualitative in the sense that it communicates the quality of life resulting from a design decision. What is proposed therefore for designers and simulationists is Quality Assurance (QA) procedures that are codified and incorporated into the design tools themselves. These are to ensure that the ‘black box’ of a digital simulation of building performance yields information that designers feel they can trust. The research demonstrates that to address the issues identified in the practitioner surveys, a Quality Control (QC) reality test is the single most important feature needed in any QA process. This would be a reality test that examines whether the ebuildings constructed with an eddst behave in a believable manner - like a ‘real’ building. The proposed Simulation QA (SimQA) approach is an internet web service. It is a database of the databases available on the internet of Quality Assured performance data. Each time a person sets up a new Quality tested eddst input file or measures a building, it becomes another “data point” - another database listed in the SimQA metadata. Also required in a robust QA process is the development of international norms for the simulation of building performance. www.aecsimqa.net is proposed as the venue for the development of an international documentation standard for simulation. Finally, modern computer-based building performance simulation has not rid the design profession of its traditional problem with design tools: that they evaluate completed designs. The proposed database will make web-accessible a set of tested building designs and their associated performance measures. Placed at the designer’s fingertips this will reveal insights into how their current building design should perform. It should be possible to generate initial design ideas based on systematic study of the successful precedents!</p>


2021 ◽  
Author(s):  
◽  
Michael Robert Donn

<p>The spur for this research was a lack of use by architecture practitioners of the environmental design decision support tools (eddst’s) they learn to use during their education. It was hypothesised that lessons for the improvement of eddst’s could be found in a systematic examination of the problems encountered by design teams with a range of currently available eddst’s. The research plan was to establish through surveys and case studies how practising architects who have tried to use building eddst’s assess the effectiveness of these tools. A range of different types of eddst was examined, each addressing a different aspect of the environment in buildings. The research did not achieve its original goal of developing a formula for the generation of new eddst’s for architects in the fields of building acoustics, lighting, thermal design and aerodynamics. What was found is a more fundamental common denominator underlying building design eddst’s: the need for built-in Quality Assurance measures that assure not only the architect, but also the simulationist and the client of the reality of the ebuilding performance predictions. It was found that contrary to their general reputation, designers do want detailed quantitative environmental information. They want to be able to discuss costs and benefits of decisions. However, they also want to be able to understand and trust this information. The output from eddst’s must therefore also be qualitative in the sense that it communicates the quality of life resulting from a design decision. What is proposed therefore for designers and simulationists is Quality Assurance (QA) procedures that are codified and incorporated into the design tools themselves. These are to ensure that the ‘black box’ of a digital simulation of building performance yields information that designers feel they can trust. The research demonstrates that to address the issues identified in the practitioner surveys, a Quality Control (QC) reality test is the single most important feature needed in any QA process. This would be a reality test that examines whether the ebuildings constructed with an eddst behave in a believable manner - like a ‘real’ building. The proposed Simulation QA (SimQA) approach is an internet web service. It is a database of the databases available on the internet of Quality Assured performance data. Each time a person sets up a new Quality tested eddst input file or measures a building, it becomes another “data point” - another database listed in the SimQA metadata. Also required in a robust QA process is the development of international norms for the simulation of building performance. www.aecsimqa.net is proposed as the venue for the development of an international documentation standard for simulation. Finally, modern computer-based building performance simulation has not rid the design profession of its traditional problem with design tools: that they evaluate completed designs. The proposed database will make web-accessible a set of tested building designs and their associated performance measures. Placed at the designer’s fingertips this will reveal insights into how their current building design should perform. It should be possible to generate initial design ideas based on systematic study of the successful precedents!</p>


2020 ◽  
pp. 323
Author(s):  
Nour Elislam Djedaa ◽  
Abderrezak Moulay Lakhdar

2007 ◽  
Vol 7 (5-6) ◽  
pp. 53-60
Author(s):  
D. Inman ◽  
D. Simidchiev ◽  
P. Jeffrey

This paper examines the use of influence diagrams (IDs) in water demand management (WDM) strategy planning with the specific objective of exploring how IDs can be used in developing computer-based decision support tools (DSTs) to complement and support existing WDM decision processes. We report the results of an expert consultation carried out in collaboration with water industry specialists in Sofia, Bulgaria. The elicited information is presented as influence diagrams and the discussion looks at their usefulness in WDM strategy design and the specification of suitable modelling techniques. The paper concludes that IDs themselves are useful in developing model structures for use in evidence-based reasoning models such as Bayesian Networks, and this is in keeping with the objectives set out in the introduction of integrating DSTs into existing decision processes. The paper will be of interest to modellers, decision-makers and scientists involved in designing tools to support resource conservation strategy implementation.


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