کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6892722 | 1445458 | 2018 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Scheduling with job-splitting considering learning and the vital-few law
ترجمه فارسی عنوان
برنامه ریزی با شغل تقسیم با توجه به یادگیری و قانون حیاتی چند
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
علوم کامپیوتر (عمومی)
چکیده انگلیسی
This research, which is motivated by real cases in labor-intensive industries where learning effects and the vital-few law take place, integrates learning and job splitting in parallel machine scheduling problems to minimize the makespan. We propose the lower bound of the problem and a job-splitting algorithm corresponding to the lower bound. Subsequently, a heuristic called SLMR is proposed based on the job-splitting algorithm with a proven worst case ratio. Furthermore, a branch-and-bound algorithm, which can obtain optimal solutions for very small problems, and a hybrid differential evolution algorithm are proposed, which can not only solve the problem, but also serve as a benchmark to evaluate the solution quality of the heuristic SLMR. The performance of the heuristic on a large number of randomly generated instances is evaluated. Results show that the proposed heuristic has good solution quality and calculation efficiency.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers & Operations Research - Volume 90, February 2018, Pages 264-274
Journal: Computers & Operations Research - Volume 90, February 2018, Pages 264-274
نویسندگان
Changchun Liu, Chenjie Wang, Zhi-hai Zhang, Li Zheng,