Optimal Assignment and Scheduling of Cranes in Slab Yard for Iron and Steel Production Enterprises

Slab yards serve as temporary slab storage between a continuous casting stage and a rolling stage. Considering non-crossing and safe clearance constraints of slab yard cranes, this work studies a multi-crane assignment and scheduling problem in the slab yard. An mixed-integer linear programming (MILP) is formulated to minimize the slab completion time. Due to its NP-hardness, the problem for large-sized instances is computationally intractable. Thus, we develop a logic-based benders decomposition algorithm (LBBD) to solve it. First, we exploit a generalized decomposition of this problem into a relaxed main problem (RMP) and a sub-problem (SP). Solving the former allocates slabs to each crane. Then, the sequence of the assigned slabs can be found by solving its corresponding sub-problem. Finally, to verify the effectiveness of LBBD, we identify a lower bound (LB) of the optimal objective function. The problem instances on real data from an iron and steel plant are created. The result of LBBD is close to such lower bound and can be found efficiently. Note to Practitioners—This work deals with a crane assignment problem with multiple cranes for handling input slabs in a slab yard. This problem is formulated as an MILP model to minimize the completion time. Its time complexity grows exponentially with the problem size. Thus, we develop a LBBD to solve it. The numerical results reveal that LBBD can find the optimal or near-optimal solution for all realistic instances in affordable computational time. Its use can ensure the high utilization of cranes and efficient service in iron and steel plants.

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Publication Details

Journal
IEEE Transactions on Automation Science and Engineering
Published
2024-11-18
DOI
https://doi.org/10.1109/tase.2024.3490039
Citations
1
Primary Topic
Advanced Manufacturing and Logistics Optimization
Type
article
Field-Weighted Citation Impact
0.35

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article

Optimal Assignment and Scheduling of Cranes in Slab Yard for Iron and Steel Production Enterprises

Qiuhong Zhao, Aiiad Albeshri, Liang Qi, Xiwang Guo et al.
1 citations
IEEE Transactions on Automation Science and Engineering
Advanced Manufacturing and Logistics Optimization
0.35
article

Optimal Assignment and Scheduling of Cranes in Slab Yard for Iron and Steel Production Enterprises

Qiuhong Zhao, Aiiad Albeshri, Liang Qi, Xiwang Guo, Shixin Liu, MengChu Zhou, Xu Wang
article en
1 citations

Abstract

Slab yards serve as temporary slab storage between a continuous casting stage and a rolling stage. Considering non-crossing and safe clearance constraints of slab yard cranes, this work studies a multi-crane assignment and scheduling problem in the slab yard. An mixed-integer linear programming (MILP) is formulated to minimize the slab completion time. Due to its NP-hardness, the problem for large-sized instances is computationally intractable. Thus, we develop a logic-based benders decomposition algorithm (LBBD) to solve it. First, we exploit a generalized decomposition of this problem into a relaxed main problem (RMP) and a sub-problem (SP). Solving the former allocates slabs to each crane. Then, the sequence of the assigned slabs can be found by solving its corresponding sub-problem. Finally, to verify the effectiveness of LBBD, we identify a lower bound (LB) of the optimal objective function. The problem instances on real data from an iron and steel plant are created. The result of LBBD is close to such lower bound and can be found efficiently. Note to Practitioners—This work deals with a crane assignment problem with multiple cranes for handling input slabs in a slab yard. This problem is formulated as an MILP model to minimize the completion time. Its time complexity grows exponentially with the problem size. Thus, we develop a LBBD to solve it. The numerical results reveal that LBBD can find the optimal or near-optimal solution for all realistic instances in affordable computational time. Its use can ensure the high utilization of cranes and efficient service in iron and steel plants.

IEEE Transactions on Automation Science and EngineeringVol. 22
Macau University of Science and Technology (MO), Liaoning Shihua University (CN), King Abdulaziz University (SA), Shenyang University of Chemical Technology (CN), Shandong University of Science and Technology (CN), Beihang University (CN), Northeastern University (CN)
National Natural Science Foundation of China, Natural Science Foundation of Shandong Province
Industry, innovation and infrastructure
Openalex Percentile: Top 34%
Advanced Manufacturing and Logistics Optimization
0.35
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