Rescheduling optimization of steelmaking-continuous casting process based on the Lagrangian heuristic algorithm

Luan, Fangjun; Mao, Ke

doi:10.3934/jimo.2016081

Cited by 13 publications

(3 citation statements)

References 34 publications

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“…We noticed that the issue related to production programs has been approached from several perspectives, as is a complex optimization problem that has been analyzed from different perspectives by different researchers. For example, models such as backpack modelling [23][24][25], the PSO-GA hybrid algorithm [26][27][28], the tabu search algorithm [29][30][31][32], the improved cuckoo search (ICS) [33][34][35], Lagrangian heuristic algorithm and other heuristic algorithms [36][37][38][39][40], the mixed model [36][37][38][39][40][41][42][43], and so on, are other types of models used in the literature.…”

Section: Dao and Marianmentioning

confidence: 99%

Application Research of Soft Computing Based on Machine Learning Production Scheduling

Fülöp

Gubán

et al. 2022

Processes

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An efficient and flexible production system can contribute to production solutions. These advantages of flexibility and efficiency are a benefit for small series productions or for individual articles. The aim of this research was to produce a genetic production system schedule similar to the sustainable production scheduling problem of a discrete product assembly plant, with more heterogeneous production lines, and controlled by one-time orders. First, we present a detailed mathematical model of the system under investigation. Then, we present the IT for a solution based on a soft calculation method. In connection with this model, a computer application was created that analyzed various versions of the model with several practical problems. The applicability of the method was analyzed with software specifically developed for this algorithm and was demonstrated on a practical example. The model handles the different products within an order, as well as their different versions. These were also considered in the solution. The solution of this model is applicable in practice, and offers solutions to better optimize production and reduce the costs of production and logistics. The developed software can not only be used for flexible production lines, but also for other problems in the supply chain that can be employed more widely (such as the problem of delivery scheduling) to which the elements of this model can be applied.

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Section: Dao and Marianmentioning

confidence: 99%

Application Research of Soft Computing Based on Machine Learning Production Scheduling

Fülöp

Gubán

et al. 2022

Processes

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“…Liu and Zhou [15] investigated an identical parallel-machine rescheduling problem, and proposed two polynomial time algorithms for lexicographically optimizing two conflicting rescheduling criteria: the total completion time and the disruption cost. Sun et al [19] investigated a hybrid flow shop rescheduling problem to minimize the total weighted completion time, the total waiting time, and the difference in the number of operations processed on different machines for different stages in the original schedule and revised schedule. There are also other references to be focused on, such as, Filar et al [10], Hoogeveen et al [13], Zhang et al [27] and Arnaout [2], etc.…”

Section: Mingbao Cheng Shuxian Xiao and Guosheng Liumentioning

confidence: 99%

Single-machine rescheduling problems with learning effect under disruptions

Cheng¹,

Xiao²,

Liu³

2018

Journal of Industrial &Amp; Management Optimization

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Rescheduling in production planning means to schedule the sequenced jobs again together with a set of new arrived jobs so as to generate a new feasible schedule, which creates disruptions to any job between the original and adjusted position. In this paper, we study rescheduling problems with learning effect under disruption constraints to minimize several classical objectives, where learning effect means that the workers gain experience during the process of operation and make the actual processing time of jobs shorter than their normal processing time. The objectives are to find optimal sequences to minimize the makespan and the total completion time under a limit of the disruptions from the original schedule. For the considered objectives under a single disruption constraint or a disruption cost constraint, we propose polynomial-time algorithms and pseudo-polynomial time algorithms, respectively.

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“…Compared with fruitful research on the SCC scheduling, less attempts have been made on the SCC rescheduling. 11,14 Existing SCC rescheduling methods includes heuristics, 15 Lagrangian relaxation, 14,16 and metaheuristics. Among the existing metaheuristics-based methods, Tang et al 10 designed a differential evolution with incremental mechanism, while Long et al 17 developed a hybrid algorithm which combined genetic algorithm and variable neighborhood search, where continuous caster breakdown was considered as the dynamic event.…”

Section: Introductionmentioning

confidence: 99%

An improved imperialist competitive algorithm for hybrid flowshop rescheduling in steelmaking-refining-continuous casting process

Peng

Deng

Zhang

et al. 2020

Measurement and Control

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Steelmaking-refining-Continuous Casting (SCC) is a key process in iron and steel production. SCC scheduling is to determine an optimal schedule for the SCC process, which is a worldwide and important problem. High-quality SCC scheduling methods will help to allocate production resources effectively and increase the productivity. However, dynamic events (e.g. machine breakdown) may happen in the realistic SCC process, which will make the SCC schedule inexecutable or not optimal. In this case, SCC rescheduling is essential in order to obtain a new optimal schedule suitable for the current production environments. The SCC rescheduling can be modeled as hybrid flowshop rescheduling. In this paper, an Improved Imperialist Competitive Algorithm (IICA) is proposed to address the SCC rescheduling. In the proposed IICA, an empire initialization is first devised for constructing an initial population with diversity and certain quality. Moreover, multiswap-based local search and imperialist competition are designed to improve the exploitation ability of the IICA, while revolution and restart strategy are devised to enhance the exploration ability of the IICA. Comparison experiments with three kinds of ICA have shown the efficiency of the IICA.

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Rescheduling optimization of steelmaking-continuous casting process based on the Lagrangian heuristic algorithm

Cited by 13 publications

References 34 publications

Application Research of Soft Computing Based on Machine Learning Production Scheduling

Application Research of Soft Computing Based on Machine Learning Production Scheduling

Single-machine rescheduling problems with learning effect under disruptions

An improved imperialist competitive algorithm for hybrid flowshop rescheduling in steelmaking-refining-continuous casting process

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