زمانبندی دو هدفه جریان کارگاهی مختلط با تقریب پارتو در یک منطقه مشخص
محورهای موضوعی :
مدیریت صنعتی
Seyed Mostafa Mousavi
1
1 - Department of Industrial Engineering, Nowshahr Branch, Islamic Azad University, Mazandaran, Iran.
تاریخ دریافت : 1396/06/18
تاریخ پذیرش : 1396/12/08
تاریخ انتشار : 1396/12/10
کلید واژه:
Hybrid flow shop,
Multi-Objective Optimization,
بهینه سازی چند هدفه,
تقریب پارتو,
جریان کارگاهی مختلط,
جمع زمان های تاخیر,
ماکزیمم زمان تکمیل,
Maximum completion time,
Pareto approximation,
Total tardiness,
چکیده مقاله :
این تحقیق، مساله زمانبندی تولید در محیط جریان کارگاهی مختلط با زمان های آماده سازی وابسته به توالی و با هدف مینیمم کردن ماکزیمم زمان تکمیل کارها و جمع زمان های تاخیر را مورد بررسی قرار می دهد. در گذشته مسائل دو هدفه با یافتن تقریب پارتو از کل فضای مساله (بدون هیچ محدودیتی) حل شده است. محدودیت در این تحقیق یافتن تقریب پارتو در یک منطقه مشخص شده است. به منظور حل مساله، الگوریتم ژنتیک چند هدفه مبتنی بر رتبه بندی پارتو مورد استفاده قرار گرفته است. در ساختار الگوریتم، دو استراتژی انتخاب جواب برای آرشیو جهت تولید پارتو در یک منطقه مشخص پیشنهاد شده است. پس از تولید مسائل نمونه، الگوریتم ژنتیک با سه استراتژی (دو استراتژی پیشنهادی و استراتژی عمومی در ادبیات) اجرا شده است. استراتژی مناسب براساس جواب-های موثر در آرشیو تعیین شده است. نتایج نشان دهنده این واقعیت است که استراتژی های پیشنهاد شده عملکرد بهتری نسبت به استراتژی در ادبیات نشان داده اند.
چکیده انگلیسی:
This paper studies the production scheduling problem in a hybrid flow shop environment with sequence-dependent setup times and the objectives of minimizing both the maximum completion time and the total tardiness. In the past, bi-objective problems were solved by finding Pareto approximation in the entire problem space (without any restrictions). The limitation in this study is to find Pareto approximation in a specified region. In order to solve the problem, multi-objective genetic algorithm based on Pareto ranking has been used. In the structure of the algorithm, two strategies have been proposed in order to select solutions for archiving and produce Pareto in a certain region. After generating sample problems, the genetic algorithm has been implemented with three strategies (two proposed and one general strategy in literature). The appropriate strategy is based on efficient solutions in the archives. The results reflect the fact that the proposed strategies have shown better performance than the literature strategy.
منابع و مأخذ:
Baker, K.R. (1974). Introduction to sequencing and scheduling. Wiley, New York.
Behnamian, J., Fatemi Ghomi, S.M.T., Zandieh, M. (2009). A multi-phase covering Pareto-optimal front method to multi-objective scheduling in a realistic hybrid flowshop using a hybrid metaheuristic. Expert Systems with Applications, 36(8): 11057–11069.
Hosseini, S.M.H. (2017). A multi-objective genetic algorithm (MOGA) for hybrid flow shop scheduling problem with assembly operation. Journal of Industrial and Systems Engineering, 10: 132-154.
Khalili, M. (2012). Multi-objective no-wait hybrid flowshop scheduling problem with transportation times. International Journal of Computational Science and Engineering, 7(2): 147-154.
Linn, R., Zhang, W. (1999). Hybrid flow shop scheduling: A Survey. Computers & Industrial Engineering, 37(1-2): 57-61.
Mousavi, S.M., Zandieh, M., Amiri, M. (2011). An efficient bi-objective heuristic for scheduling of hybrid flow shops. International Journal of Advanced Manufacturing Technology, 54(1): 287-307.
Mousavi, S.M., Mahdavi, I., Rezaeian, J. Zandieh, M. (2016).An efficient bi-objective algorithm to solve re-entrant hybrid flow shop scheduling with learning effect and setup times. Operational Research International Journal, https://doi.org/10.1007/s12351-016-0257-6.
Rashidi, E., Jahandar, M., Zandieh, M. (2010).An improved hybrid multi-objective parallel genetic algorithm for hybrid flow shop scheduling with unrelated parallel machines. International Journal of Advanced manufacturing Technology, 49(9): 1129–1139.
Tran, T.H., Ng, K.M. (2013). A Hybrid water flow algorithm for multi-objective flexible flow shop scheduling problem. Engineering Optimization, 45(4): 483-502.
Ying, K.C., Lin, S.W., Wan, S.Y. (2014). Bi-objective reentrant hybrid flowshop scheduling: an iterated Pareto greedy algorithm. International Journal of Production Research, 52(19): 5735-5747.
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Baker, K.R. (1974). Introduction to sequencing and scheduling. Wiley, New York.
Behnamian, J., Fatemi Ghomi, S.M.T., Zandieh, M. (2009). A multi-phase covering Pareto-optimal front method to multi-objective scheduling in a realistic hybrid flowshop using a hybrid metaheuristic. Expert Systems with Applications, 36(8): 11057–11069.
Hosseini, S.M.H. (2017). A multi-objective genetic algorithm (MOGA) for hybrid flow shop scheduling problem with assembly operation. Journal of Industrial and Systems Engineering, 10: 132-154.
Khalili, M. (2012). Multi-objective no-wait hybrid flowshop scheduling problem with transportation times. International Journal of Computational Science and Engineering, 7(2): 147-154.
Linn, R., Zhang, W. (1999). Hybrid flow shop scheduling: A Survey. Computers & Industrial Engineering, 37(1-2): 57-61.
Mousavi, S.M., Zandieh, M., Amiri, M. (2011). An efficient bi-objective heuristic for scheduling of hybrid flow shops. International Journal of Advanced Manufacturing Technology, 54(1): 287-307.
Mousavi, S.M., Mahdavi, I., Rezaeian, J. Zandieh, M. (2016).An efficient bi-objective algorithm to solve re-entrant hybrid flow shop scheduling with learning effect and setup times. Operational Research International Journal, https://doi.org/10.1007/s12351-016-0257-6.
Rashidi, E., Jahandar, M., Zandieh, M. (2010).An improved hybrid multi-objective parallel genetic algorithm for hybrid flow shop scheduling with unrelated parallel machines. International Journal of Advanced manufacturing Technology, 49(9): 1129–1139.
Tran, T.H., Ng, K.M. (2013). A Hybrid water flow algorithm for multi-objective flexible flow shop scheduling problem. Engineering Optimization, 45(4): 483-502.
Ying, K.C., Lin, S.W., Wan, S.Y. (2014). Bi-objective reentrant hybrid flowshop scheduling: an iterated Pareto greedy algorithm. International Journal of Production Research, 52(19): 5735-5747.
