دانلود مقاله ISI انگلیسی شماره 46777
ترجمه فارسی عنوان مقاله

بهینه سازی پیکربندی پلت فرم با تغییر نسلی

عنوان انگلیسی
Optimization of a platform configuration with generational changes
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
46777 2015 11 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : International Journal of Production Economics, Volume 169, November 2015, Pages 299–309

ترجمه کلمات کلیدی
سکو - شاخص انواع نسل - محاسبه DNA شبیه سازی شده - الگوریتم ژنتیک
کلمات کلیدی انگلیسی
Platform; Generation variety index; Simulated DNA computation; Genetic algorithm
پیش نمایش مقاله
پیش نمایش مقاله  بهینه سازی پیکربندی پلت فرم با تغییر نسلی

چکیده انگلیسی

Platform is an established strategy for producing customized products while managing the economy of scale. Innovation in various areas makes different components in a platform outdated or redundant within a short span of time. This poses severe challenge to the robustness of the platform configuration that efficiently satisfies the volatile needs of the customers from various segments. Therefore, deciding the platform configuration that can adequately accommodate generational changes in the product design is emerging as a new challenge. This paper deals with optimization of a platform configuration through a couple of product generations. For this, specifications from different customers and their probable attribute changes are mapped to product׳s utility, which signifies importance of each component through a period of time. Utility by cost ratio for different products forms the basic variable for optimizing the configuration of a platform. An illustrative example is detailed to demonstrate the methodology adopted in exploring the optimal platform configuration. This paper incorporates an intelligent DNA-based technique to reach the optimal configuration. The results of simulated DNA computation are compared with that of genetic algorithm (GA). The results show significant improvement in the number of objective function evaluations before reaching the optimal result, against that of GA thus establishing its superiority in numerical optimization.