A self-learning hyper-heuristic for the distributed assembly blocking flow shop scheduling problem with total flowtime criterion. (November 2022)

• Record Type: Journal Article
• Authors: Zhao, Fuqing; Di, Shilu; Wang, Ling; Xu, Tianpeng; Zhu, Ningning; Jonrinaldi,
• Is Part Of: Engineering applications of artificial intelligence. Volume 116(2022)
• Publication Date: 2022-11
• Subjects: Distributed assembly -- Blocking flow shop scheduling -- Total flowtime -- Constructive heuristic -- Hyper-heuristic -- Problem-specific knowledge; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.105418
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: The distributed assembly blocking flow shop scheduling problem, which is a significant scenario in modern supply chains and manufacturing systems, has attracted significant attention from researchers and practitioners. To formulate the problem, a mixed-integer linear programming model is introduced to optimize the total flowtime. A constructive heuristic (HHNRa) and a self-learning hyper-heuristic (SLHH) are proposed to address the scheduling problem. HHNRa is designed based on the problem-specific knowledge to obtain initial solutions with high quality. A self-learning high-level strategy based on the historical success rate of low-level heuristics is presented to manipulate the low-level heuristics to operate in the solution space. In addition, a restart scheme with three distinct constructive heuristics is utilized to maintain the diversity of the solution. Based on 900 small-scale benchmark instances and 810 large-scale benchmark instances, comprehensive numerical experiments are conducted to evaluate the performance of the proposed SLHH algorithm. The results of the statistical analysis indicate that the proposed self-learning hyper-heuristic is superior to the compared state-of-the-art algorithms for the problem under consideration. Consequently, the proposed constructive heuristic and the self-learning hyper-heuristic are effective methods for the distributed assembly blocking flow shop scheduling problem. Highlights: A self-learning hyper-heuristic is proposed to solve the DABFSP. The characteristics of the DABFSP is extracted to construct an initial solution. The historical information of LLH is utilized to guide the selection of the next LLH. The restart scheme is introduced to keep the diversity of solutions. The experimental results demonstrated the validity of the SLHH.