An Integrated System for Production Scheduling in Steelmaking and Casting Plants

Fanti, Maria Pia; Rotunno, Giuliana; Stecco, Gabriella; Ukovich, Walter; Mininel, Stefano

doi:10.1109/tase.2015.2477362

Cited by 26 publications

(20 citation statements)

References 14 publications

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“…A similar version of the problem considered in this paper was presented by Fanti et al. () who proposed an integrated system consisting of a database, an optimization engine, a simulation module, and a user interface for the scheduling problem of a specific plant. The optimization engine models the scheduling as a hybrid flow shop using a mixed integer linear programming (MILP) formulation.…”

Section: Introductionmentioning

confidence: 99%

“…With respect to Fanti et al. (), we have made several modifications (see Section 3) to better represent the real‐world specifications, including the presence of the border data , coming from the previous schedule. In addition, in order to obtain a model that captures the essential features of the problem, we have simplified the problem statement, so as to remove low level details too specific for the situation at hand.…”

Section: Introductionmentioning

confidence: 99%

“…On the other side, we have generalized the problem including features specified by Danieli Automation that were not included in the original model of Fanti et al. (), but are common across different plants.…”

Section: Introductionmentioning

confidence: 99%

“…We collected a dataset of real‐world instances coming from the same plant of Fanti et al. (), but with a different and larger set of operating conditions. Instances and results are available at https://opthub.uniud.it, and hopefully could be used as a benchmark for future researches and comparisons, given also the lack of available ones in the literature.…”

Section: Introductionmentioning

confidence: 99%

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Modeling and solving the steelmaking and casting scheduling problem

Armellini

Borzone

Ceschia

et al. 2018

Int Trans Operational Res

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We propose a general model for the problem of planning and scheduling steelmaking and casting activities obtained by combining common features and constraints of the operations from a real plant and the literature. For tackling the problem, we develop a simulated annealing approach based on a solution space made of job permutations, which uses as submodule a chronological constructive procedure that assigns processing times and resources to jobs. Our technique, properly tuned in a statistically principled way, is able to find good solutions for a large range of different settings and horizons. In addition, it outperforms both a greedy procedure and a constraint‐based solver developed for comparison purposes on almost all instances. Finally, we have collected several real‐world instances that we make available on the web along with the solution validator and our best results.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling and solving the steelmaking and casting scheduling problem

Armellini

Borzone

Ceschia

et al. 2018

Int Trans Operational Res

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“…Paper [3] introduced a computerized scheduling model for continuous casting in steel plants. Paper [4] established an integrated system for steel ladle scheduling. Paper [5] analyzed the impact of ladles on steelmaking scheduling.…”

Section: Introductionmentioning

confidence: 99%

Steelmaking-Casting of Molten Steel by Decarburization Ladle Matching

Liu

Pang

et al. 2018

Mathematical Problems in Engineering

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Steelmaking-continuous casting is a complex process. The method of selecting a ladle, which also functions as a storage device, follows a specific process of the production plan. In ladle matching, several ladle attributes are considered. However, matching objectives are difficult to achieve simultaneously. Different molten steel properties have contributed to the complexity of matching constraints, and, thus, matching optimization is regarded a multiconflict goal problem. In the process of optimization, the first-order rule learning method is first used to extract key ladle attributes (performance indicators), including highest temperature, usage frequency, lowest-level material, and outlet. On the basis of a number of indicators, such as ladle temperature, quantity, material, and usage frequency, as well as skateboard quantity, the ladle matching model is established. Second, the rule of ladle selection is determined by the method of least-generalization rule learning. Third, a simulation experiment is carried out according to various scheduling order strategies and matching priority combinations. Finally, the heuristic ladle matching method based on the rule priority (RP) is determined for possible industrial applications. Results show that the accuracy of ladle selection can be improved. In particular, the numbers of ladles and maintenance times are reduced. Consequently, furnace production efficiency is also enhanced.

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Ladle intelligent re-scheduling method in steelmaking–refining–continuous casting production process based on BP neural network working condition estimation

Liu

Pang

2022

Int J Adv Manuf Technol

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The ladle re-scheduling of steelmaking and continuous casting production aims at continuously casting many charges with the same cast and avoiding conflicts of adjacent charges on the same machine. It proposes a method of ladle re-scheduling in the production process of steelmaking-refining-continuous casting, including two parts: plan re-scheduling and ladle optimisation scheduling. Firstly, a re-scheduling optimisation model of the steelmaking and continuous casting production is built, which aims at minimising the waiting time of all charges. The re-scheduling strategy of steelmaking and continuous casting production is proposed by interval processing time of charges and scheduling expert experience. The re-scheduling strategy is composed of two parts: re-scheduling charge decision and charge processing machine decision. Then, the first-order rule learning is used to select the optimisation target to establish the ladle optimal scheduling model. The ladle matching rules are extracted by the rule reasoning of the minimum general generalisation. The ladle optimisation scheduling method consisting of the optimal selection of the ladle and the preparation of the optimal path of the ladle is proposed. Ladle selection is based on the production process and adopts rule-based reasoning to select decarburised ladle after choosing dephosphorised ladle. Ladle path preparation, a multi-priority heuristic method, is designed to decide the path of the ladle from the converter to the refining furnace to the continuous casting machine. Finally, it was actually verified based on a large-scale steel company in Shanghai, China. Results showed that the production efficiency of steelmaking-refining-continuous casting was improved.

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An Integrated System for Production Scheduling in Steelmaking and Casting Plants

Cited by 26 publications

References 14 publications

Modeling and solving the steelmaking and casting scheduling problem

Modeling and solving the steelmaking and casting scheduling problem

Steelmaking-Casting of Molten Steel by Decarburization Ladle Matching

Ladle intelligent re-scheduling method in steelmaking–refining–continuous casting production process based on BP neural network working condition estimation

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