An Improved Artificial Bee Colony algorithm for real-world hybrid flowshop rescheduling in Steelmaking-refining-Continuous Casting process. (August 2018)

• Record Type: Journal Article
• Authors: Peng, Kunkun; Pan, Quan-Ke; Gao, Liang; Zhang, Biao; Pang, Xinfu
• Is Part Of: Computers & industrial engineering. Volume 122(2018)
• Page Start: 235
• Page End: 250
• Publication Date: 2018-08
• Subjects: Artificial Bee Colony -- Steelmaking-refining-Continuous Casting -- Hybrid flowshop rescheduling -- Swarm intelligence -- Variable neighboring operator; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2018.05.056
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: A real-world hybrid flowshop rescheduling problem in SCC process is studied. An Improved ABC (IABC) with new encoding and decoding approaches is developed. A charge left-shifting strategy is proposed in the decoding to decrease cast break. Initialization, variable neighboring operator, worst solution updating are devised. The strength and availability of the IABC is demonstrated by extensive experiments. Abstract: Realistic production systems usually encounter various of unexpected disruptions, which may invalidate the original schedules, and thus rescheduling becomes essential. This paper studies a real-world hybrid flowshop rescheduling problem in Steelmaking-refining-Continuous Casting (SCC) process, where machine breakdown is considered as the disruption, and controllable processing times in the last stage of SCC process is considered. An Improved Artificial Bee Colony (IABC) algorithm is developed to solve the problem. In the IABC, novel encoding and decoding strategies are devised to represent the solutions effectively, where a charge left-shifting strategy is designed to decrease cast break. A population initialization heuristic is devised to generate solutions with a high level of quality and diversity. Meanwhile, a variable neighboring operator, which can balance the exploration and exploitation abilities, is proposed to generate new solutions with high quality for the employed bee and onlooker bee phases. Moreover, a worst solution replacement strategy is developed to further enhance the exploitation ability. To demonstrate the performance of the IABC, comprehensive computational comparisons against several state-of-the-art algorithms and statistical analysis are conducted, which discloses the strength and availability of the IABC. Moreover, key features of the IABC are analyzed, confirming their critical roles to the success of the IABC.