دانلود مقاله ISI انگلیسی شماره 43704
ترجمه فارسی عنوان مقاله

مسئله فروشنده دوره گرد همراه با پنجره زمانی: تطبیق الگوریتم ها از زمان سفر به زمان کامل شدن پردازش بهینه سازی

عنوان انگلیسی
The travelling salesman problem with time windows: Adapting algorithms from travel-time to makespan optimization ☆
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
43704 2013 10 صفحه PDF
منبع

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

Journal : Applied Soft Computing, Volume 13, Issue 9, September 2013, Pages 3806–3815

ترجمه کلمات کلیدی
بهینه سازی کلونی مورچه - روش فشرده شبیه سازی آنیلینگ - مسئله فروشنده دوره گرد همراه با پنجره زمانی - هیبریداسیون
کلمات کلیدی انگلیسی
Ant colony optimization; Compressed simulated annealing; Travelling salesman problem with time windows; Hybridization
پیش نمایش مقاله
پیش نمایش مقاله  مسئله فروشنده دوره گرد همراه با پنجره زمانی: تطبیق الگوریتم ها از زمان سفر به زمان کامل شدن پردازش بهینه سازی

چکیده انگلیسی

In combinatorial optimization it is not rare to find problems whose mathematical structure is nearly the same, differing only in some aspect related to the motivating application. For example, many problems in machine scheduling and vehicle routing have equivalent formulations and only differ with respect to the optimization objective, or particular constraints. Moreover, while some problems receive a lot of attention from the research community, their close relatives receive hardly any attention at all. Given two closely related problems, it is intuitive that it may be effective to adapt state-of-the-art algorithms—initially introduced for the well-studied problem variant—to the less-studied problem variant. In this paper we provide an example based on the travelling salesman problem with time windows that supports this intuition. In this context, the well-studied problem variant minimizes the travel time, while the less-studied problem variant minimizes the makespan. Indeed, the results show that the algorithms that we adapt from travel-time minimization to makespan minimization significantly outperform the existing state-of-the-art approaches for makespan minimization.