Set-based concept selection in multi-objective problems: optimality versus variability approach

Avigad, Gideon; Moshaiov, Amiram

doi:10.1080/09544820701802279

Cited by 25 publications

(26 citation statements)

References 25 publications

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“…Sophisticated, combinatorial optimization has increased insight into design uncertainty, but still provides more sophisticated point solutions. Recently, Avigad (2007) introduced the need to have a SBD quantitative solution structure that balances both optimality in a point solution sense with variability to capture set solutions in clustered set solutions. Resilience in the PD process is supported by utilizing SBD to create multidesign sets that increase confidence to attain meeting design target requirements.…”

Section: Discussionmentioning

confidence: 99%

“…The need for a framework that couples SBD with mathematical analytics is both real and overdue. Additionally, the concept to optimize, while keeping solution variance in concept, sets has been proposed …”

Section: Literature Review and Insightsmentioning

confidence: 99%

“…Additionally, the concept to optimize, while keeping solution variance in concept, sets has been proposed. 10 Very recent promising research ties mixed uncertainty robust optimization with both random and interval uncertainties. 11 The design space is expanded beyond the point solution by using the uncertainty performance deviation to create a sensitivity region through the usage of hybrid indices.…”

Section: Literature Review and Insightsmentioning

confidence: 99%

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Product development resilience through set‐based design

Rapp

Chinnam

Doerry

et al. 2018

Systems Engineering

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Often during a system Product Development program, external factors or requirements change, forcing system design change. This uncertainty adversely affects program outcome, often adding to development time and cost, production cost, and can compromise system performance. We present a development approach that minimizes the impacts, by proactively considering the possibility of changes in the external factors and the implications of mid‐course design changes. The approach considers the set of alternative designs and the burdens of a mid‐course change from one design to another in determining the relative value of a specific design through the set‐based design methodology. The approach considers and plans parallel (redundant) development of alternative designs with progressive selection of options, including time‐versus‐cost tradeoffs and the impact change‐costs. The approach includes a framework of the development process addressing design and integration lead‐times, their relationship to the time‐order of design decisions, and the time‐dependent burden of design changes. We also compare set‐based and single point design schemes.

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Section: Discussionmentioning

confidence: 99%

Section: Literature Review and Insightsmentioning

confidence: 99%

Section: Literature Review and Insightsmentioning

confidence: 99%

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Product development resilience through set‐based design

Rapp

Chinnam

Doerry

et al. 2018

Systems Engineering

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“…The process of conceptualization, as illustrated in Fig. 12, is a human creativity-driven preliminary design stage dealing with the formulation of an idea or concept which determines the scope of a project in terms of desired design features and requirements [42][43][44]. Typically, numerous alternative approaches or conceptual designs will be proposed and analyzed before agreeing upon the single most suitable one.…”

Section: Partial Overlap In Phenotype Spacementioning

confidence: 99%

Evolutionary Multitasking: A Computer Science View of Cognitive Multitasking

2016

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The human mind possesses the most remarkable ability to perform multiple tasks with apparent simultaneity. In fact, with the present-day explosion in the variety and volume of incoming information streams that must be absorbed and appropriately processed, the opportunity, tendency, and (even) the need to multitask are unprecedented. Thus, it comes as little surprise that the pursuit of intelligent systems and algorithms that are capable of efficient multitasking is rapidly gaining importance among contemporary scientists who are faced with the increasing complexity of real-world problems. To this end, the present paper is dedicated to a detailed exposition on a so-far underexplored characteristic of population-based search algorithms, i.e., their inherent ability (much like the human mind) to handle multiple optimization tasks at once. We present a simple evolutionary methodology capable of cross-domain multitask optimization in a unified genotype space and show that there exist many potential benefits of its application in practical domains. Most notably, it is revealed that multitasking enables one to automatically leverage upon the underlying commonalities between distinct optimization tasks, thereby providing the scope for considerably improved performance in real-world problem solving.

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“…Research that has compared strategies exists (e.g. Avigad and Moshaiov 2009), and significant and detailed research that has examined the efficiency and convergence properties of automated algorithms also exists. For example, Marler and Arora (2004) provided a comprehensive survey of strategies that use continuous nonlinear multiobjective optimisation.…”

Section: Introductionmentioning

confidence: 98%

Design exploration assessment methodology: testing the guidance of design processes

Clevenger

Haymaker

Ehrich

2013

Journal of Engineering Design

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This paper introduces the design exploration assessment methodology (DEAM) for comparing design process impact and outcome. Current practice fails to reliably generate high-performing alternatives in part because it lacks systematic means to compare existing or emerging design processes. Researchers lack empirical methods and data to evaluate design challenges and the strategies available to address them. In this paper, we document and then apply the DEAM to the professional implementation of six design strategies across two design challenges using the charrette test method. The results are used to compare the strategies according to the performance of the solution(s) generated. For the strategies and challenges investigated, more information during design does not always assist the designer to produce better performing alternatives. We discuss possible explanations and conclude with a discussion of the strengths and weaknesses of the DEAM as an evaluation method. Initial findings demonstrate that the DEAM is a method capable of providing a meaningful comparison of strategies in the domain of energyefficient design challenges. Terms ComponentsVariable: a design choice to be made. A variable can be discrete (i.e. number of windows) or continuous (i.e. building length). Option: individual variable input(s) (i.e. number of windows = {1, 2, or 3}; building length = 10-20 m). Decision: the selection of an option (i.e. number of windows = 2; building length = 12.75 m). Alternative: a combination of decisions about options. Stakeholder: a party with a stake in the selection of alternatives. Goal: declaration of intended properties of alternatives. Downloaded by [Northeastern University] at 13:12 03 December 2014 166 C.M. Clevenger et al.Preference: weight assigned to a goal by a stakeholder. Constraint: limit placed on options. Impact: alternative's estimated performance according to a specified goal. Requirement: limit placed on impacts. Objective: union of stakeholders, goals, preferences, and constraints. Value: net performance of an alternative relative to all objectives. DimensionsChallenge: a set of decisions to be made ranging from simple to complex. Strategy: a procedure to generate decisions ranging from none to advanced. Exploration: a history of decisions made ranging from misled to guided. Design Process: implementation of a strategy to a challenge resulting in an exploration. Guidance: variation in exploration produced by applying different strategies to a given challenge. SpacesObjective space: set of stakeholders, goals, preferences, and constraints. Alternative space: feasible (explored or unexplored) alternatives for a given challenge. (similar to option space, Struck et al. 2009). Impact space: analysed impacts of alternatives relative to goals, determined to be acceptable or unacceptable according to the requirements. Value space: values of the set of alternatives generated during an exploration. We use italics throughout this paper to indicate explicit reference to these definitions.

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Set-based concept selection in multi-objective problems: optimality versus variability approach

Cited by 25 publications

References 25 publications

Product development resilience through set‐based design

Product development resilience through set‐based design

Evolutionary Multitasking: A Computer Science View of Cognitive Multitasking

Design exploration assessment methodology: testing the guidance of design processes

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