دانلود مقاله ISI انگلیسی شماره 78854
ترجمه فارسی عنوان مقاله

پیدا کردن استراتژی های بهینه در بازی Stackelberg چند-دوره چند-رهبر-پیرو با استفاده از یک الگوریتم تکاملی

عنوان انگلیسی
Finding optimal strategies in a multi-period multi-leader–follower Stackelberg game using an evolutionary algorithm
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
78854 2014 12 صفحه PDF
منبع

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

Journal : Computers & Operations Research, Volume 41, January 2014, Pages 374–385

ترجمه کلمات کلیدی
نظریه بازی؛ بهینه سازی دوسطحی؛ بازی Stackelberg؛ مشکل چند-رهبر-پیرو؛ الگوریتم تکاملی
کلمات کلیدی انگلیسی
Game theory; Bilevel optimization; Stackelberg games; Multi-leader–follower problem; Evolutionary algorithm
پیش نمایش مقاله
پیش نمایش مقاله  پیدا کردن استراتژی های بهینه در بازی Stackelberg چند-دوره چند-رهبر-پیرو با استفاده از یک الگوریتم تکاملی

چکیده انگلیسی

Stackelberg games are a classic example of bilevel optimization problems, which are often encountered in game theory and economics. These are complex problems with a hierarchical structure, where one optimization task is nested within the other. Despite a number of studies on handling bilevel optimization problems, these problems still remain a challenging territory, and existing methodologies are able to handle only simple problems with few variables under assumptions of continuity and differentiability. In this paper, we consider a special case of a multi-period multi-leader–follower Stackelberg competition model with non-linear cost and demand functions and discrete production variables. The model has potential applications, for instance in aircraft manufacturing industry, which is an oligopoly where a few giant firms enjoy a tremendous commitment power over the other smaller players. We solve cases with different number of leaders and followers, and show how the entrance or exit of a player affects the profits of the other players. In the presence of various model complexities, we use a computationally intensive nested evolutionary strategy to find an optimal solution for the model. The strategy is evaluated on a test-suite of bilevel problems, and it has been shown that the method is successful in handling difficult bilevel problems.