An improved artificial bee colony algorithm for steelmaking-refining-continuous casting scheduling problem

doi:10.1016/j.cjche.2018.06.008

Chinese Journal of Chemical Engineering ›› 2018, Vol. 26 ›› Issue (8): 1727-1735.DOI: 10.1016/j.cjche.2018.06.008

• Selected Papers from the 28th Chinese Process Control Conference • 上一篇下一篇

An improved artificial bee colony algorithm for steelmaking-refining-continuous casting scheduling problem

Kunkun Peng, Quanke Pan, Biao Zhang

State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China

收稿日期:2017-10-11 修回日期:2018-06-01 出版日期:2018-08-28 发布日期:2018-09-21
通讯作者: Quanke Pan,E-mail addresses:panquanke@hust.edu.cn
作者简介:Kunkun Peng,E-mail addresses:pengkunkun@126.com;Biao Zhang,E-mail addresses:zhangbiao1218@gmail.com
基金资助:
Supported by the National Natural Science Foundation of China (51705177, 51575212), the Program for New Century Excellent Talents in University (NCET-13-0106), and the Program for HUST Academic Frontier Youth Team.

An improved artificial bee colony algorithm for steelmaking-refining-continuous casting scheduling problem

Kunkun Peng, Quanke Pan, Biao Zhang

State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China

Received:2017-10-11 Revised:2018-06-01 Online:2018-08-28 Published:2018-09-21
Contact: Quanke Pan,E-mail addresses:panquanke@hust.edu.cn
Supported by:
Supported by the National Natural Science Foundation of China (51705177, 51575212), the Program for New Century Excellent Talents in University (NCET-13-0106), and the Program for HUST Academic Frontier Youth Team.

摘要/Abstract

摘要： Steelmaking-refining-Continuous Casting (SCC) scheduling is a worldwide problem, which is NP-hard. Effective SCC scheduling algorithms can help to enhance productivity, and thus make significant monetary savings. This paper develops an Improved Artificial Bee Colony (IABC) algorithm for the SCC scheduling. In the proposed IABC, charge permutation is employed to represent the solutions. In the population initialization, several solutions with certain quality are produced by a heuristic while others are generated randomly. Two variable neighborhood search neighborhood operators are devised to generate new high-quality solutions for the employed bee and onlooker bee phases, respectively. Meanwhile, in order to enhance the exploitation ability, a control parameter is introduced to conduct the search of onlooker bee phase. Moreover, to enhance the exploration ability, the new generated solutions are accepted with a control acceptance criterion. In the scout bee phase, the solution corresponding to a scout bee is updated by performing three swap operators and three insert operators with equal probability. Computational comparisons against several recent algorithms and a state-of-the-art SCC scheduling algorithm have demonstrated the strength and superiority of the IABC.

关键词: Artificial bee colony, Steelmaking-refining-continuous casting, Hybrid flowshop scheduling, Variable neighborhood search

Abstract: Steelmaking-refining-Continuous Casting (SCC) scheduling is a worldwide problem, which is NP-hard. Effective SCC scheduling algorithms can help to enhance productivity, and thus make significant monetary savings. This paper develops an Improved Artificial Bee Colony (IABC) algorithm for the SCC scheduling. In the proposed IABC, charge permutation is employed to represent the solutions. In the population initialization, several solutions with certain quality are produced by a heuristic while others are generated randomly. Two variable neighborhood search neighborhood operators are devised to generate new high-quality solutions for the employed bee and onlooker bee phases, respectively. Meanwhile, in order to enhance the exploitation ability, a control parameter is introduced to conduct the search of onlooker bee phase. Moreover, to enhance the exploration ability, the new generated solutions are accepted with a control acceptance criterion. In the scout bee phase, the solution corresponding to a scout bee is updated by performing three swap operators and three insert operators with equal probability. Computational comparisons against several recent algorithms and a state-of-the-art SCC scheduling algorithm have demonstrated the strength and superiority of the IABC.

Key words: Artificial bee colony, Steelmaking-refining-continuous casting, Hybrid flowshop scheduling, Variable neighborhood search

Kunkun Peng, Quanke Pan, Biao Zhang. An improved artificial bee colony algorithm for steelmaking-refining-continuous casting scheduling problem[J]. Chinese Journal of Chemical Engineering, 2018, 26(8): 1727-1735.

Kunkun Peng, Quanke Pan, Biao Zhang. An improved artificial bee colony algorithm for steelmaking-refining-continuous casting scheduling problem[J]. Chin.J.Chem.Eng., 2018, 26(8): 1727-1735.

参考文献

[1] H. Cui, X. Luo, An improved Lagrangian relaxation approach to scheduling steelmaking-continuous casting process, Comput. Chem. Eng. 106(2017) 133-146.

[2] Q. Pan, L. Wang, K. Mao, et al., An effective artificial bee colony algorithm for a realworld hybrid flowshop problem in steelmaking process, IEEE Trans. Autom. Sci. Eng. 10(2) (2013) 307-322.

[3] I. Harjunkoski, I.E. Grossmann, A decomposition approach for the scheduling of a steel plant production, Comput. Chem. Eng. 25(2001) 1647-1660.

[4] D. Pacciarelli, M. Pranzo, Production scheduling in a steelmaking-continuous casting plant, Comput. Chem. Eng. 28(12) (2004) 2823-2835.

[5] Q. Pan, An effective co-evolutionary artificial bee colony algorithm for steelmaking-continuous casting scheduling, Eur. J. Oper. Res. 250(3) (2016) 702-714.

[6] H. Xuan, L. Tang, Scheduling a hybrid flowshop with batch production at the last stage, Comput. Oper. Res. 34(9) (2007) 2718-2733.

[7] L. Tang, J. Liu, A. Rong, et al., A mathematical programming model for scheduling steelmaking-continuous casting production, Eur. J. Oper. Res. 120(2) (2000) 423-435.

[8] J. Li, Q. Pan, K. Mao, et al., Solving the steelmaking casting problem using an effective fruit fly optimisation algorithm, Knowl.-Based Syst. 2(2014) 28-36.

[9] S. Yu, T. Chai, Heuristic scheduling method for steelmaking and continuous casting production process, Control Theory A 33(11) (2016) 1413-1421.

[10] S. Yu, Q. Pan, A rescheduling method for operation time delay disturbance in steelmaking and continuous casting production process, J. Iron Steel Res. Int. 19(12) (2012) 33-41.

[11] S. Yu, T. Chai, Y. Tang, An effective heuristic rescheduling method for steelmaking and continuous casting production process with multirefining modes, IEEE Trans. Syst. Man Cybern. Syst. Hum. 46(12) (2016) 1675-1688.

[12] K. Mao, Q. Pan, X. Pang, et al., A novel Lagrangian relaxation approach for a hybrid flowshop scheduling problem in the steelmaking-continuous casting process, Eur. J. Oper. Res. 236(2014) 51-60.

[13] K. Mao, Q. Pan, X. Pang, et al., An effective Lagrangian relaxation approach for rescheduling a steelmaking-continuous casting process, Control Eng. Pract. 30(2014) 67-77.

[14] J. Li, Q. Pan, K. Mao, A hybrid fruit fly optimization algorithm for the realistic hybrid flowshop rescheduling problem in steelmaking systems, IEEE Trans. Autom. Sci. Eng. 13(2) (2016) 932-949.

[15] L. Tang, Y. Zhao, J. Liu, An improved differential evolution algorithm for practical dynamic scheduling in steelmaking-continuous casting production, IEEE Trans. Evol. Comput. 18(2) (2014) 209-225.

[16] S. Jiang, M. Liu, J. Hao, et al., A bi-layer optimization approach for a hybrid flow shop scheduling problem involving controllable processing times in the steelmaking industry, Comput. Ind. Eng. 87(2015) 518-531.

[17] J. Hao, M. Liu, S. Jiang, et al., A soft-decision based two-layered scheduling approach for uncertain steelmaking-continuous casting process, Eur. J. Oper. Res. 244(3) (2015) 966-979.

[18] J. Long, Z. Zheng, X. Gao, Dynamic scheduling in steelmaking-continuous casting production for continuous caster breakdown, Int. J. Prod. Res. 55(11) (2017) 3197-3216.

[19] L. Cui, G. Li, Z. Zhu, et al., A novel artificial bee colony algorithm with an adaptive population size for numerical function optimization, Inf. Sci. 414(2017) 53-67.

[20] I. Khan, M.K. Maiti, A swap sequence based Artificial Bee Colony algorithm for Traveling Salesman Problem, Swarm Evol. Comput. (2018) 1-11.

[21] D. Karaboga, B. Gorkemli, C. Ozturk, et al., A comprehensive survey:Artificial bee colony (ABC) algorithm and applications, Artif. Intell. Rev. 42(1) (2014) 21-57.

[22] X. Li, S. Ma, Multiobjective discrete artificial bee colony algorithm for multiobjective permutation flow shop scheduling problem with sequence dependent setup times, IEEE Trans. Eng. Manag. 64(2) (2017) 149-165.

[23] S. Sundar, P.N. Suganthan, C.T. Jin, et al., A hybrid artificial bee colony algorithm for the job-shop scheduling problem with no-wait constraint, Soft. Comput. 21(5) (2017) 1193-1202.

[24] N. Sharma, H. Sharm, A. Sharma, Beer froth artificial bee colony algorithm for jobshop scheduling problem, Appl. Soft Comput. 68(2018) 507-524.

[25] L. Wang, G. Zhou, Y. Xu, et al., An effective artificial bee colony algorithm for the flexible job-shop scheduling problem, Int. J. Adv. Manuf. Technol. 60(2012) 303-315.

[26] X. Li, Z. Peng, B. Du, et al., Hybrid artificial bee colony algorithm with a rescheduling strategy for solving flexible job shop scheduling problems, Comput. Ind. Eng. 113(2017) 10-26.

[27] J. Li, Q. Pan, Solving the large-scale hybrid flow shop scheduling problem with limited buffers by a hybrid artificial bee colony algorithm, Inf. Sci. 316(2015) 487-502.

[28] Q. Pan, M.F. Tasgetiren, P.N. Suganthan, et al., A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem, Inf. Sci. 181(12) (2011) 2455-2468.

[29] D. Gong, Y. Han, J. Sun, A novel hybrid multi-objective artificial bee colony algorithm for blocking lot-streaming flow shop scheduling problems, Knowl.-Based Syst. 148(2018) 115-130.

[30] A. Yurtkuran, E. Emel, A discrete artificial bee colony algorithm for single machine scheduling problems, Int. J. Prod. Res. 54(22) (2016) 6860-6878.

[31] R. Zhang, P. Chang, S. Song, et al., A multi-objective artificial bee colony algorithm for parallel batch-processing machine scheduling in fabric dyeing processes, Knowl.-Based Syst. 116(2017) 114-129.

[32] D. Karaboga, B. Akay, A comparative study of Artificial Bee Colony algorithm, Appl. Math. Comput. 214(1) (2009) 108-132.

[33] D. Lei, X. Guo, Hybrid flow shop scheduling with not-all-machines options via local search with controlled deterioration, Comput. Oper. Res. 65(2016) 76-82.

[34] P. Hansen, N. Mladenovic, J.A. Moreno Perez, Variable neighbourhood search:Methods and applications, Ann. Oper. Res. 175(1) (2010) 367-407.

[35] R. Ruiz, C. Maroto, A genetic algorithm for hybrid flowshops with sequence dependent setup times and machine eligibility, Eur. J. Oper. Res. 169(3) (2006) 781-800.

[36] X. Dong, H. Huang, P. Chen, An iterated local search algorithm for the permutation flowshop problem with total flowtime criterion, Comput. Oper. Res. 36(5) (2009) 1664-1669.

[37] H. Zheng, L. Wang, X. Zheng, Teaching-learning-based optimization algorithm for multi-skill resource constrained project scheduling problem, Soft. Comput. 21(6) (2017) 1537-1548.

An improved artificial bee colony algorithm for steelmaking-refining-continuous casting scheduling problem

An improved artificial bee colony algorithm for steelmaking-refining-continuous casting scheduling problem

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 1

编辑推荐

Metrics

本文评价