کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4944224 | 1437982 | 2017 | 22 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Consolidated optimization algorithm for resource-constrained project scheduling problems
ترجمه فارسی عنوان
الگوریتم بهینه سازی تلفیقی برای مشکلات برنامه ریزی پروژه با محدودیت منابع
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کلمات کلیدی
مشکلات برنامه ریزی پروژه با محدودیت منابع، الگوریتمهای تکاملی، چند الگوریتم، چند اپراتور،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
Resource-constrained project scheduling problems (RCPSPs) represent an important class of practical problems. Over the years, many optimization algorithms for solving them have been proposed, with their performances evaluated using well-established test instances with various levels of complexity. While it is desirable to obtain a high-quality solution and fast rate of convergence from an optimization algorithm, no single one performs well across the entire space of instances. Furthermore, even for a given algorithm, the optimal choice of its operators and control parameters may vary from one problem to another. To deal with this issue, we present a generic framework for solving RCPSPs in which various meta-heuristics, each with multiple search operators, are self-adaptively used during the search process and more emphasis is placed on the better-performing algorithms, and their underlying search operators. To further improve the rate of convergence and introduce good-quality solutions into the population earlier, a local search approach is introduced. The experimental results clearly indicate the capability of the proposed algorithm to attain high-quality results using a small population. Compared with several state-of-the-art algorithms, the proposed one delivers the best solutions for problems with 30 and 60 activities, and is very competitive for those involving 120 activities.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Sciences - Volumes 418â419, December 2017, Pages 346-362
Journal: Information Sciences - Volumes 418â419, December 2017, Pages 346-362
نویسندگان
Saber Elsayed, Ruhul Sarker, Tapabrata Ray, Carlos Coello Coello,