Ergonomic Chair Design by Fusing Qualitative and Quantitative Criteria using Interactive Genetic Algorithms

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

Integrated qualitativeness in design by multi-objective optimization and interactive evolutionary computation.

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

Roadmap to Self-Serving Assets in Civil Aerospace

Organised by: Cranfield UniversityThe “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.Mori Seiki – The Machine Tool Compan

Evaluation of sequential, multi-objective, and parallel interactive genetic algorithms for multi-objective optimization problems.

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

The Effect of User Interaction Mechanisms in Multi-objective IGA

In this paper four mechanisms, fine and coarse grained fitness rating, linguistic evaluation and active user intervention are compared for use in the multi-objective IGA. The interaction mechanisms are tested on the ergonomic chair design problem. The active user intervention mechanism provided the best fitness convergence but resulted in the least diverse results. The fine grained evaluation provided a good blend of fitness convergence and diversity while the popular coarse grained discrete rating provided poor results. Linguistic evaluation resulted in poor qualitative fitness despite its fast speed of evaluation. The significant differences between interaction mechanisms show the need for further research

Evaluation of Sequential, Multi-objective, and Parallel Interactive Genetic Algorithms for Multi-objective Floor Plan Optimisation

We propose a sequential IGA, multi-objective IGA and parallel interactive genetic algorithm (IGA), and evaluate them with a multi-objective floor planning task through both simulation and real IGA users. Combining human evaluation with an optimization system for engineering design enables us to embed domain specific knowledge which is frequently hard to describe, subjective criteria and preferences in engineering design. We introduce IGA technique to extend previous approaches with sequential single objective GA and multi-objective GA. We also introduce parallel IGA newly. Experimental results show that (1) the multi-objective IGA and the parallel IGA clearly provide better results than the sequential IGA, and (2) the multi-objective IGA provides more diverse results and faster convergence for a floor planning task although the parallel IGA provides better fitness convergence

Evaluation of sequential, multi-objective, and parallel interactive genetic algorithms for multi-objective optimization problems

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. / Keywords: innovative design, subjectivity, evolutionary computin

Ergonomic Chair Design by Fusing Qualitative and Quantitative Criteria Using Interactive Genetic Algorithms

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 multiobjective 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 multiobjective approaches show promising results in fitness convergence, design diversity, and user satisfaction metrics