A prediction-based online soft scheduling algorithm for the real-world steelmaking-continuous casting production

Jiang, Sheng-Long; Liu, Min; Lin, Jian-hua; Zhong, Huaxing

doi:10.1016/j.knosys.2016.08.010

Cited by 34 publications

(5 citation statements)

References 36 publications

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“…However, proactive studies seem to be a more recent approach, starting on 2008 and growing in frequency during the following years, while reactive scheduling solutions are present during the whole period covered by this review. Only a few authors consider the possibility of combining both approaches in their works (hybrid approaches), such as Jiang et al [53], Worapradya et al [17,27] or Yu et al [41]. This can be explained by the focus in recent years on the application of metaheuristics and simulation solutions to reactive approaches (see Table 2) which could be motivated by the necessity of improving the solutions to be executed dynamically in real production environments.…”

Section: Scheduling Solutions In Steel Sector Considering Uncertaintymentioning

confidence: 99%

“…Guirong and Qiqiang [56] apply the Cross Entropy (CE) algorithm in a two-layer approach to solve the steelmaking scheduling problem, with an outer layer to calculate the basic processing times and the start casting times, and an inner layer to calculate the machine assignment of the charges. Jiang et al [53] introduce a combined proactive and reactive solution for steelmaking scheduling, combining rescheduling capabilities with Gaussian Process Regression (GPR) procedure to predict the characteristic indexes (slack ratios) aiming to enhance the robustness of the initial schedule of their model against uncertainties. On a similar problem they also propose a continuous Estimation Distribution Algorithm (EDA) [58] applied in two phases: the first EDA calculates the slack ratios as characteristic indexes while a second phase EDA combined with Local Search optimizes the schedule of the jobs at the continuous casting stage.…”

Section: Scheduling Solutions In Steel Sector Considering Uncertaintymentioning

confidence: 99%

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Planning and Scheduling with Uncertainty in the Steel Sector: A Review

et al. 2019

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The following paper proposes a study about the existing solutions for dealing with uncertainty while solving the planning and scheduling problem at steel industry manufacturing processes. The different techniques designed to cope with uncertainty in manufacturing scheduling are discussed, along with the main uncertainty factors affecting the scheduling. The paper proposes a classification for the main uncertainties affecting the steelmaking process and analyzes the existing literature about solutions for the scheduling with uncertainty in the steel sector in terms of approaches followed and uncertainty types considered. Finally, the main remarks and future challenges within this field are presented.

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Section: Scheduling Solutions In Steel Sector Considering Uncertaintymentioning

confidence: 99%

Section: Scheduling Solutions In Steel Sector Considering Uncertaintymentioning

confidence: 99%

Planning and Scheduling with Uncertainty in the Steel Sector: A Review

et al. 2019

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“…The formulation of a steel production scheduling scheme is the key to the stability of steel production and the smooth flow of logistics [2]. Optimal scheduling of steelmaking production contributes to boosting productivity, reducing costs, and achieving sustainable manufacturing for an integrated steel company [3,4]. Therefore, in the field of steel production, it is often necessary to develop and prioritize scheduling schemes.…”

Section: Introductionmentioning

confidence: 99%

Novel Multi-Criteria Group Decision Making Method for Production Scheduling Based on Group AHP and Cloud Model Enhanced TOPSIS

Zhang,

Lv,

Liu

et al. 2024

Processes

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Optimized production scheduling can greatly improve efficiency and reduce waste in the steel manufacturing industry. With the increasing demands on the economy, the environment, and society, more and more factors need to be considered in the production scheduling process. Currently, only a few methods are developed for the comprehensive evaluation and prioritization of scheduling schemes. This paper proposes a novel MCGDM (multi-criteria group decision making) method for the ranking and selection of production scheduling schemes. First, a novel indicator system involving both qualitative and quantitative indicators is put forward. Diverse statistical methods and evaluation functions are proposed for the evaluation of quantitative indicators. The evaluation method of qualitative indicators is proposed based on heterogeneous data, cloud model theory, and group decision-making techniques. Then, a novel Group AHP model is proposed to determine the weights of all evaluation indicators. Finally, a novel cloud-model-enhanced TOPSIS (technique for order of preference by similarity to ideal solution) method is proposed to rank alternative production scheduling schemes. A practical example is presented to show the implementation details and demonstrate the feasibility of our proposed method. The results and comparative analysis indicate that our hybrid MCGDM method is more reasonable, flexible, practical, and effective in evaluating and ranking production scheduling schemes in an uncertain environment.

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“…Furthermore, in real environments, the problems that arise are random and dynamic, so the parameters, limitations, and objectives change over time [38]. In general, the different contingencies in steelmaking production can be divided into three classes [39]. Firstly, there are contingencies that are resource related [40], such as the shortage of raw material, a lack of adequate personnel to carry out the necessary tasks or even machine breakdowns.…”

Section: Introductionmentioning

confidence: 99%

Development of a Steel Plant Rescheduling Algorithm Based on Batch Decisions

et al. 2021

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During the steelmaking and continuous casting process in the steel plant, it is common to encounter delays that affect initial planning. Furthermore, continuous casting machines themselves can lose much of their performance in the event of closure of one or more of their casting strands. The situation that is generated, far from being a planning problem, forces consideration of a vision of cost analysis when deciding changes in the planned sequences. This study presents a detailed analysis of the different circumstances that can cause strands closures or sequence breaks, their consequences and the different options available to minimize losses. Finally, an algorithm capable of analyzing the workshop situation and making the most favorable decision to optimize production is proposed, analyzed and compared with the efficiency of the original scheduling method in a real steel plant. The new algorithm proves its efficiency in all situations, with a time-saving average of 26.41 min per decision taken.

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A prediction-based online soft scheduling algorithm for the real-world steelmaking-continuous casting production

Cited by 34 publications

References 36 publications

Planning and Scheduling with Uncertainty in the Steel Sector: A Review

Planning and Scheduling with Uncertainty in the Steel Sector: A Review

Novel Multi-Criteria Group Decision Making Method for Production Scheduling Based on Group AHP and Cloud Model Enhanced TOPSIS

Development of a Steel Plant Rescheduling Algorithm Based on Batch Decisions

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