A proactive scheduling approach to steel rolling process with stochastic machine breakdown

Wang, Dujuan; Liu, Feng; Jin, Yaochu

doi:10.1007/s11047-016-9599-5

Cited by 15 publications

(7 citation statements)

References 48 publications

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“…Zandieh et al 5 conducted an integrated optimization study on shop scheduling and preventive maintenance in FJSP by focusing on maximum completion time and system unavailability as optimization objectives. Considering the single-machine system problem, Wang et al 6 designed a multi-objective evolutionary algorithm based on the support vector regression proxy index to determine the processing order of a workpiece and the input of equipment maintenance resources. The flow shop scheduling problem with random failures was studied by Zhang et al 7 .…”

Section: Related Workmentioning

confidence: 99%

A robust optimal scheduling system based on multi-performance driving for complex manufacturing systems

Yu,

Zhang,

Zhao

et al. 2023

Sci Rep

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A robust optimal scheduling method driven by multi-objects is proposed for the collaborative optimization problem between dynamic scheduling, preventive maintenance of equipment, and robustness of scheduling schemes in a complex manufacturing system. Firstly, the equipment maintenance task is mapped to the process level, and composite dispatching rules with weight parameters are designed, which flexibly consider equipment maintenance and system processing status. Secondly, the performance-driven ideology is analyzed through two models based on the IWOA-MLP algorithm. Thirdly, the feedback mechanism ideology facilitates adaptive closed-loop optimizations. Finally, a series of experiments were carried out on the simulation platform of a semiconductor manufacturing enterprise in Shanghai. The experimental results show that the proposed robust optimal scheduling system can effectively deal with mixed uncertainty, improve production performances, and maintain highly robust measures.

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Section: Related Workmentioning

confidence: 99%

A robust optimal scheduling system based on multi-performance driving for complex manufacturing systems

Yu,

Zhang,

Zhao

et al. 2023

Sci Rep

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show abstract

“…A common approach to deal with uncertainty in machine scheduling problems is stochastic programming (SP), which assumes that the distributions of random variables are known (like normal 24 or exponential 25 distribution). To enhance the robustness and hedge against the risk of unacceptable scenarios, Ranjbar et al 26 and Pishevar et al 27 applied β-robust method to the parallel machine scheduling problem, in which the processing time follows a normal distribution and the likelihood of exceeding some performance measure is minimized.…”

Section: Related Workmentioning

confidence: 99%

Optimization of a molten iron scheduling problem with uncertain processing time using variable neighborhood search algorithm

Liu

Chang

Song

2022

Sci Rep

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Punctuality of the steel-making scheduling is important to save steel production costs, but the processing time of the pretreatment process, which connects the iron- and steel-making stages, is usually uncertain. This paper presents a distributionally robust iron-steel allocation (DRISA) model to obtain a robust scheduling plan, where the distribution of the pretreatment time vector is assumed to belong to an ambiguity set which contains all the distributions with given first and second moments. This model aims to minimize the production objective by determining the iron-steel allocation and the completion time of each charge, while the constraints should hold with a certain probability under the worst-case distribution. To solve problems in large-scale efficiently, a variable neighborhood algorithm is developed to obtain a near-optimal solution in a short time. Experiments based on actual production data demonstrate its efficiency. Results also show the robustness of the DRISA model, i.e., the adjustment and delay of the robust schedule derived from the DRISA model are less than the nominal one.

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“…In another study (Nwanya et al, 2017), the authors apply multiple regression for optimizing machine downtime in plastic manufacturing. Wang et al (2017) also applied multiple regression analysis to evaluate downtimes and other equipment performance characteristics, such as cycle time, capacity, weight and overall equipment effectiveness (OEE). Table 2 presents the comparison of this study with the previous downtime studies.…”

Section: Related Workmentioning

confidence: 99%

ERIM: An ensemble of rare itemset mining and its application in the automotive industry

2022

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Discovering previously unknown anomalies that are rare and dramatically differ from the majority of the data is a critical need for the automotive industry. Rare itemset mining (RIM), one of the pattern‐based methods, has been used for anomaly detection due to providing successful analysis results. However, several aspects still need to be explored, such as improving the mining process by identifying more targeted, valuable and reliable rare itemsets. Motivated by this fact, this study proposes a novel approach, named ensemble of rare itemset mining (ERIM), which investigates weak rare itemsets (WRIs) using different algorithms and aggregates these rules to obtain strong rare itemsets (SRIs). This study also combines four different RIM algorithms (Apriori Rare, Apriori Inverse, CORI and RP‐Growth) as base learners for the first time. The proposed ERIM approach is a general methodology that can be applied to any field, but, in this study, it was used in the automotive industry as a case study. In the experiments, ERIM was applied to a real‐world gear manufacturing dataset to discover anomalies in machine downtimes. The experimental results were evaluated in terms of the number of itemsets and the length of itemsets by giving some samples, as well. The results showed that the proposed ERIM approach gives more reliable common knowledge by jointly considering the relation between WRIs discovered by the base learners. The findings indicated that the proposed ERIM technique was successful in detecting anomalies whose support values are below 7.12. Furthermore, it is clear from the experimental results that the ERIM discovered the highest number of SRIs, 1403, each of which is a 3‐itemset. Finally, the results showed that our method performed 43.37% better on average than state‐of‐the‐art methods on the same dataset.

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A proactive scheduling approach to steel rolling process with stochastic machine breakdown

Cited by 15 publications

References 48 publications

A robust optimal scheduling system based on multi-performance driving for complex manufacturing systems

A robust optimal scheduling system based on multi-performance driving for complex manufacturing systems

Optimization of a molten iron scheduling problem with uncertain processing time using variable neighborhood search algorithm

ERIM: An ensemble of rare itemset mining and its application in the automotive industry

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