دانلود مقاله ISI انگلیسی شماره 49355
ترجمه فارسی عنوان مقاله

تطبیق بهینه سازی چندهدفه برای مبادلات یک شات چندشاخصه با تخفیف مقداری در کارگزاری الکترونیک

عنوان انگلیسی
Multi-objective optimization matching for one-shot multi-attribute exchanges with quantity discounts in E-brokerage
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
49355 2011 12 صفحه PDF
منبع

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

Journal : Expert Systems with Applications, Volume 38, Issue 4, April 2011, Pages 4169–4180

ترجمه کلمات کلیدی
کارگزاری الکترونیک - مبادلات چندشاخصه - بهینه سازی چندهدفه - تخفیف مقداری - الگوریتم ژنتیک - بازپخت شبیه سازی شده
کلمات کلیدی انگلیسی
E-brokerage; Multi-attribute exchanges; Multi-objective optimization; Quantity discounts; Genetic algorithm; Simulated annealing
پیش نمایش مقاله
پیش نمایش مقاله  تطبیق بهینه سازی چندهدفه برای مبادلات یک شات چندشاخصه با تخفیف مقداری در کارگزاری الکترونیک

چکیده انگلیسی

Electronic brokerages (E-brokerages) are Internet-based organizations that enable buyers and sellers to do business with each other. While E-brokerages have become a significant sector of E-commerce, theory and guidelines for matching the multi-attribute exchange in E-brokerage are sparse. This paper presents an approach to optimize the matching of one-shot multi-attribute exchanges with quantity discounts. Firstly, based on the conception and definition of matching degree and quantity discount, a multi-objective optimization model is proposed to maximize the matching degree and trade volume. This model belongs to a class of multi-objective nonlinear transportation problems and cannot be solved effectively by conventional methods, especially when large-scale problems are involved. Hence, secondly, a novel hybrid multi-objective meta-heuristic algorithm named multi-objective simulated annealing genetic algorithm (MOSAGA) has been developed to solve the proposed model. Finally, the computational results and analyses of some numerical problems are given to illustrate the application and performance of the proposed model and algorithm.