Hybrid Firefly Algorithms for Multi-objective Permutation Flow Shop Scheduling Problem

PEI Xiaobing; ZHANG Rui; YU Xiuyan

doi:10.13976/j.cnki.xk.2020.9369

Volume 49 Issue 4

Aug. 2020

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INFORMATION AND CONTROL > 2020 > 49(4): 478-488. > DOI: 10.13976/j.cnki.xk.2020.9369 CSTR: 32166.14.xk.2020.9369

PEI Xiaobing, ZHANG Rui, YU Xiuyan. Hybrid Firefly Algorithms for Multi-objective Permutation Flow Shop Scheduling Problem[J]. INFORMATION AND CONTROL, 2020, 49(4): 478-488. DOI: 10.13976/j.cnki.xk.2020.9369

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Hybrid Firefly Algorithms for Multi-objective Permutation Flow Shop Scheduling Problem

School of management, Tianjin University of Technology, Tianjin 300384, China

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Received Date: July 04, 2019
Revised Date: May 29, 2020
Accepted Date: September 29, 2020
Available Online: December 01, 2022
Published Date: August 19, 2020

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Abstract

Abstract

To solve the multi-objective permutation flow shop scheduling problem, a hybrid algorithm based on the firefly algorithm is proposed. The hybrid algorithm considers the firefly algorithm as the framework, and the NEH model and machine coding are used to initialize the population, thereby ensuring the diversity of the initial population while improving the quality of the initial population. The information between workpieces and the information between workpieces and machines are captured by the probability matrix. In addition, the information in the probability matrix is used to combine blocks, and the blocks are used to improve the convergence speed and increase the diversity of feasible solutions. Simulation results of Reeves suites in OR-library and a comparison with other excellent algorithms validate the efficiency of the proposed algorithm.

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References (21)

References

[1]	Xu J, Wu C C, Yin Y, et al. An iterated local search for the mul-objective permutation flowshop scheduling problem with sequence-dependent setup times[J]. Applied Soft Computing, 2017, 52: 39-47. doi: 10.1016/j.asoc.2016.11.031
[2]	Scaria A, George K, Sebastian J. An artificial bee colony approach for multi-objective job shop scheduling[J]. Procedia Technology, 2016, 25: 1030-1037. doi: 10.1016/j.protcy.2016.08.203
[3]	Laribi I, Yalaoui F, Belkaid F, et al. Heuristics for solving flow shop scheduling problem under resources constraints[J]. IFAC-PapersOnLine, 2016, 49(12): 1478-1483. doi: 10.1016/j.ifacol.2016.07.780
[4]	Zhang R, Chiong R. Solving the energy-efficient job shop scheduling problem: A multi-objective genetic algorithm with enhanced local search for minimizing the total weighted tardiness and total energy consumption[J]. Journal of Cleaner Production, 2016, 112: 3361-3375. doi: 10.1016/j.jclepro.2015.09.097
[5]	Khorasanian D, Moslehi G. Two-machine flow shop scheduling problem with blocking, multi-task flexibility of the first machine, and preemption[J]. Computers & Operations Research, 2017, 79: 94-108. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=795be18b532b7d0cd08b9cc910be3984
[6]	Sharma N, Sharma H, Sharma A. Beer froth artificial bee colony algorithm for job-shop scheduling problem[J]. Applied Soft Computing, 2018, 68: 507-524. doi: 10.1016/j.asoc.2018.04.001
[7]	Ishibuchi H, Murata T. A multi-objective genetic local search algorithm and its application to flow shop scheduling[J]. IEEE Transactions on Systems, Man and Cybernetics-Part C: Application and Reviews, 1998, 28: 392-403. doi: 10.1109/5326.704576
[8]	Ishibuchi H, Murata T. A multi-objective genetic local search algorithm and its application to flow shop scheduling[J]. IEEE Transactions on Systems, Man and Cybernetics-Part C: Application and Reviews, 1998, 28: 392-403. doi: 10.1109/5326.704576
[9]	Fattahi P. Multi-objective meta-heuristics to solve three-stage assembly flow shop scheduling problem with machine availability constraints[J]. International Journal of Production Research, 2015, 53(3): 944-968. doi: 10.1080/00207543.2014.948575
[10]	Li X, Ma S. Multi-objective memetic search algorithm for multi-objective permutation flow shop scheduling problem[J]. IEEE Access, 2017, 4: 2154-2165. http://ieeexplore.ieee.org/document/7467399/
[11]	徐文婕, 朱光宇.直觉模糊集相似度遗传算法求解多目标车间调度问题[J].控制理论与应用, 2019, 36(7): 1057-1066. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=kzllyyy201907005 Xu W J, Zhu G Y. Intuitionistic fuzzy set similarity genetic algorithm for solving multi-objective shop scheduling problem[J]. Control Theory and Application, 2019, 36(7): 1057-1066. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=kzllyyy201907005
[12]	Lu C, Gao L, Li X, et al. Energy-efficient permutation flow shop scheduling problem using a hybrid multi-objective backtracking search algorithm[J]. Journal of Cleaner Production, 2017, 144: 228-238. doi: 10.1016/j.jclepro.2017.01.011
[13]	齐学梅, 王宏涛, 杨洁.量子萤火虫算法及在无等待流水调度上的应用[J].信息与控制, 2016, 45(2): 211-217. doi: 10.13976/j.cnki.xk.2016.0211 Qi X M, Wang H T, Yang J, et al. Quantum firefly algorithm and its application in waiting free flow scheduling[J]. Information and Control, 2016, 45(2): 211-217 doi: 10.13976/j.cnki.xk.2016.0211
[14]	张军丽, 周永权.人工萤火虫与差分进化混合优化算法[J].信息与控制, 2011, 40(5): 608-613. http://ic.sia.cn/CN/abstract/abstract9956.shtml Zhang J L, Zhou Y Q. Hybrid optimization algorithm of artificial firefly and differential evolution[J]. Information and Control, 2011, 40(5): 608-613 http://ic.sia.cn/CN/abstract/abstract9956.shtml
[15]	熊娟, 文桦.基于萤火虫搜索算法的图像纹理特征提取研究[J].计量学报, 2016, 37(3): 255-259. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jlxb98201603007 Xiong J, Wen H. Research on texture feature extraction based on firefly search algorithm[J]. Journal of Metrology, 2016, 37(3): 255-259. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jlxb98201603007
[16]	Pinedo M, Hadavi K. Scheduling: Theory, algorithms, and systems[J]. AIIE Transactions, 2016, 28(8):695-697. doi: 10.1007/978-3-642-46773-8_5
[17]	黄霞, 叶春明, 曹磊.多目标置换流水车间调度的混沌杂草优化算法[J].系统工程理论与实践, 2017, 37(1): 253-262. CNKI:SUN:XTLL.0.2017-01-022 Huang X, Ye C M, Cool, et al. Chaos weed optimization algorithm for multi-objective displacement flow shop scheduling[J]. System Engineering Theory and Practice, 2017, 37(1): 253-262 CNKI:SUN:XTLL.0.2017-01-022
[18]	Chang P C, Huang W H, Ting C J. Dynamic diversity control in genetic algorithm for mining unsearched solution space in TSP problems.[J]. Expert Systems with Applications, 2010, 37(3): 1863-1878. doi: 10.1016/j.eswa.2009.07.066
[19]	Zitzler E. Evolutionary algorithms for multiobjective optimization: Methods and applications[M]. Aachen: Shaker Verlag, 1999.
[20]	Deb K, Pratap A, Agarwal S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197. doi: 10.1109/4235.996017
[21]	陈慧芬.基于链结学习的子群体进化算法求解多目标调度问题[D].天津: 天津理工大学, 2017. CNKI:CDMD:2.1017.814820 Chen H F. Sub population evolutionary algorithm based on link learning to solve multi-objective scheduling problem[D]. Tianjin: Tianjin University of Technology, 2017. CNKI:CDMD:2.1017.814820