A decomposition-based two-stage online scheduling approach and its integrated system in the hybrid flow shop of steel industry. (1st March 2023)

• Record Type: Journal Article
• Authors: Jiang, Sheng-Long; Xu, Chuanpei; Zhang, Long; Ma, Yong
• Is Part Of: Expert systems with applications. Volume 213:Part C(2023)
• Publication Date: 2023-03-01
• Subjects: Online scheduling -- System architecture -- Hybrid flow shop -- Constraint programming -- Decomposition; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.119200
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Dynamic scheduling is a challenge for implementing smart manufacturing in the steel industry. A novel online scheduling model based on two-stage uncertain optimization is formulated. A decomposition-based algorithm combined MILP and CP is presented. An integrated scheduling system is developed and implemented in a real-world steel plant. Abstract: Steelmaking-continuous casting (SCC) is one of the most critical building blocks in the modern steel industry. Many random events occur in the real-world SCC production system. In this paper, we propose a two-stage online scheduling policy that protects the baseline schedule by the slacks provided by intra-flow times and casting speeds. The main task of the two-stage online scheduling model is: (1) to make "here-and-now" decisions for minimizing economic costs and penalties caused by constraint violations; (2) make "wait-and-see" decisions for online scheduling. Afterward, we propose a decomposition-based optimization algorithm that divides the online scheduling problem into a master problem (MP) to seek partial solutions at the last processing stage and a slave problem (SP) to check optimal solutions for upstream processing stages. Then, we employ the IBM ILOG CPLEX to solve MP and use the constraint programming (CP) optimizer to solve SP. Sensitivity analyses and algorithm comparisons are conducted on a set of well-synthetic and realistic instances to validate the proposed model and algorithm. The results show that the proposed online scheduling model and algorithm can solve realistic industrial case studies. Finally, we also develop a scheduling system integrating the proposed model and algorithm.