المؤلفون: Tyson R. Browning, Ali A. Yassine, Christoph Meier

المصدر: Journal of Mechanical Design. 129:566-585

مصطلحات موضوعية: Engineering, Mathematical optimization, Product design, business.industry, Mechanical Engineering, Schedule (project management), Design structure matrix, Computer Graphics and Computer-Aided Design, Computer Science Applications, Mechanics of Materials, Search algorithm, Genetic algorithm, Local search (optimization), Information flow (information theory), business, Engineering design process, Algorithm

الوصف: In product design, it is critical to perform project activities in an appropriate sequence. Otherwise, essential information will not be available when it is needed, and activities that depend on it will proceed using assumptions instead. Later, when the real information is finally available, comparing it with the assumptions made often precipitates a cascade of rework, and thus cost and schedule overruns for the project. Information flow models have been used to sequence the engineering design process to minimize feedback and iteration, i.e., to maximize the availability of real information where assumptions might otherwise be made instead. In this paper, we apply Genetic Algorithms (GAs) to an information flow model to find an optimized sequence for a set of design activities. The optimality of a solution depends on the objective of rearrangement. In an activity sequencing context, objectives vary: reducing iteration/feedback, increasing concurrency, reducing development lead-time and cost, or some combination of these. We adopt a matrix-based representation scheme, the design structure matrix (DSM), for the information flow models. Our tests indicate that certain DSM characteristics (e.g., size, sparseness, and sequencing objective) cause serious problems for simple Genetic Algorithm (SGA) designs. To cope with the SGA deficiency, we investigate the use of a competent GA: the ordering messy GA (OmeGA). Tests confirm the superiority of the OmeGA over a SGA for hard DSM problems. Extensions enhancing the efficiency of both a SGA and the OmeGA, in particular, niching and hybridization with a local search method, are also investigated.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::57e95635121a4e02ea63b411354a8733Test
https://doi.org/10.1115/1.2717224Test

المؤلفون: Christoph Meier, Ali A. Yassine, Tyson R. Browning

المصدر: Journal of Mechanical Design. Jun2007, Vol. 129 Issue 6, p566-585. 20p.

مصطلحات موضوعية: *COMBINATORIAL optimization, *GENETIC algorithms, *MATRICES (Mathematics)

مستخلص: In product design, it is critical to perform project activities in an appropriate sequence. Otherwise, essential information will not be available when it is needed, and activities that depend on it will proceed using assumptions instead. Later, when the real information is finally available, comparing it with the assumptions made often precipitates a cascade of rework, and thus cost and schedule overruns for the project. Information flow models have been used to sequence the engineering design process to minimize feedback and iteration, i.e., to maximize the availability of real information where assumptions might otherwise be made instead. In this paper, we apply Genetic Algorithms (GAs) to an information flow model to find an optimized sequence for a set of design activities. The optimality of a solution depends on the objective of rearrangement. In an activity sequencing context, objectives vary: reducing iteration/feedback, increasing concurrency, reducing development lead-time and cost, or some combination of these. We adopt a matrix-based representation scheme, the design structure matrix (DSM), for the information flow models. Our tests indicate that certain DSM characteristics (e.g., size, sparseness, and sequencing objective) cause serious problems for simple Genetic Algorithm (SGA) designs. To cope with the SGA deficiency, we investigate the use of a competent GA: the ordering messy GA (OmeGA). Tests confirm the superiority of the OmeGA over a SGA for hard DSM problems. Extensions enhancing the efficiency of both a SGA and the OmeGA, in particular, niching and hybridization with a local search method, are also investigated. [ABSTRACT FROM AUTHOR]