کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
5127646 1489058 2017 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
An effective and efficient heuristic for no-wait flow shop production to minimize total completion time
موضوعات مرتبط
مهندسی و علوم پایه سایر رشته های مهندسی مهندسی صنعتی و تولید
پیش نمایش صفحه اول مقاله
An effective and efficient heuristic for no-wait flow shop production to minimize total completion time
چکیده انگلیسی


- We present a current and future idle time (CFI) heuristic for no-wait flow shop production.
- We use the initial sequence algorithm and neighborhood exchanging method to improve effectiveness.
- We use the objective increment method to reduce the computational complexity.
- We show that our CFI heuristic is more effective than three typical heuristics.
- We can improve the efficiency of operating room scheduling using our CFI heuristic.

No-wait flow shop production has been widely applied in manufacturing. However, minimization of total completion time for no-wait flow shop production is NP-complete. Consequently, achieving good effectiveness and efficiency is a challenge in no-wait flow shop scheduling, where effectiveness means the deviation from optimal solutions and efficiency means the computational complexity or computation time. We propose a current and future idle time (CFI) constructive heuristic for no-wait flow shop scheduling to minimize total completion time. To improve effectiveness, we take current idle times and future idle times into consideration and use the insertion and neighborhood exchanging techniques. To improve efficiency, we introduce an objective increment method and determine the number of iterations to reduce the computation time. Compared with three recently developed heuristics, our CFI heuristic can achieve greater effectiveness in less computation time based on Taillard's benchmarks and 600 randomly generated instances. Moreover, using our CFI heuristic for operating room (OR) scheduling, we decrease the average patient flow times by 11.2% over historical ones in University of Kentucky Health Care (UKHC).

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers & Industrial Engineering - Volume 108, June 2017, Pages 57-69
نویسندگان
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