Browsing by Author "Brintrup, Alexandra Melike"

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    Ergonomic Chair Design by Fusing Qualitative and Quantitative Criteria using Interactive Genetic Algorithms
    (IEEE, 2008-06-01T00:00:00Z) Brintrup, Alexandra Melike; Ramsden, Jeremy J.; Takagi, Hideyuki
    This paper emphasizes the necessity of formally bringing qualitative and quantitative criteria of ergonomic design together, and provides a novel complementary design framework with this aim. Within this framework, different design criteria are viewed as optimization objectives; and design solutions are iteratively improved through the cooperative efforts of computer and user. The framework is rooted in multi-objective optimization, genetic algorithms and interactive user evaluation. Three different algorithms based on the framework are developed, and tested with an ergonomic chair design problem. The parallel and multi-objective approaches show promising results in fitness convergence, design diversity and user satisfaction metrics.
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    Evaluation of sequential, multi-objective, and parallel interactive genetic algorithms for multi-objective optimization problems.
    (Jointly by, Collegium Basilea (Institute of Advanced Study) and Association of Modern Scientific Investigation., 2006-09) Brintrup, Alexandra Melike; Takagi, Hideyuki; Tiwari, Ashutosh; Ramsden, Jeremy J.
    We propose a sequential interactive genetic algorithm (IGA), multi-objective IGA and parallel IGA, and evaluate them with both simulated and real users. Combining human evaluation with an optimization system for engineering design enables us to embed domainspecific knowledge that is frequently hard to describe, i.e. subjective criteria, and design preferences. We introduce a new IGA technique to extend the previously introduced sequential single objective GA and multi-objective GA, viz. parallel IGA. Experimental evaluation of three algorithms with a multi-objective manufacturing plant layout design task shows that the multi-objective IGA and the parallel IGA clearly provide better results than the sequential IGA, and that the multi-objective IGA gives the most diverse results and fastest convergence to a stable set of qualitatively optimum solutions, although the parallel IGA provides the best quantitative fitness convergence.
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    Integrated qualitativeness in design by multi-objective optimization and interactive evolutionary computation.
    (2005-09-01T00:00:00Z) Brintrup, Alexandra Melike; Ramsden, Jeremy J.; Tiwari, Ashutosh
    Abstract- The concept of qualitativeness in design is an important one, and needs to be incorporated in the optimization process for a number of reasons outlined in this paper. Interactive Evolutionary Computation and Fuzzy Systems are two of the widely used approaches for handling qualitativeness in design optimization. This paper classifies the types of qualitativeness observed in design optimization, makes the case for their necessity, and proposes a novel framework for handling them, combining the two approaches in an evolutionary multi-objective optimization platform. Two components of the framework are tested using the floor-planning problem, and observations are reported. Future work is defined onthe development of the framework.
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    Roadmap to Self-Serving Assets in Civil Aerospace
    (Cranfield University Press, 2009-04-01) Brintrup, Alexandra Melike; Ranasinghe, D. C.; Kwan, S.; Parlikad, A.; Owens, K.; Rajkumar Roy; Essam Shehab
    The “intelligent object” paradigm first occurred in holonic manufacturing, where objects managed their production. The “self-serving asset” is a further evolution of those early concepts from manufacturing to usage phase. The usage phase bestows a different set of requirements including maximisation of the asset’s life-in-service and benefits to the asset’s stakeholders. Addressing these requirements with a selfserving asset may lead to more streamlined decision-making in service operations, reduce erroneous or suboptimal decisions, and enable condition-based maintenance. We present a future direction for service systems by considering self-serving assets in the aerospace industry, and outline a technology roadmap for the transformation.