Adaptive Preventive Maintenance for Flow Shop Scheduling With Resumable Processing

Ye, Honghan; Wang, Xi; Liu, Kaibo

doi:10.1109/tase.2020.2978890

Cited by 24 publications

(7 citation statements)

References 43 publications

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“…For example, machine breakdown may happen at any time. So, it is also necessary to consider a maintenance plan along with real-time scheduling [34].…”

Section: Fig 2 Smart Manufacturing Modulesmentioning

confidence: 99%

“…Maintenance activities are usually non-separable with production scheduling [32], [33], [35]. Based on [34], there are two main groups of maintenance: corrective maintenance (which involves repair during unexpected machine breakdown) and preventive maintenance (a condition where a planned schedule is executed before machine breakdown happens). On the other hand, the study in [14] classified the industrial maintenance strategies into four: reactive (perform maintenance during complete machine failure); corrective (identify and solve failures when it happens before total machine failure); preventive (performing regular maintenance to prevent partial or complete failure); and predictive (anticipate failures before it happens and guess the remaining useful life of the machine).…”

Section: Fig 2 Smart Manufacturing Modulesmentioning

confidence: 99%

See 1 more Smart Citation

Scheduling Algorithms: Challenges Towards Smart Manufacturing

Workneh

Gmira

2022

Int. j. electr. comput. eng. syst. (Online)

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Collecting, processing, analyzing, and driving knowledge from large-scale real-time data is now realized with the emergence of Artificial Intelligence (AI) and Deep Learning (DL). The breakthrough of Industry 4.0 lays a foundation for intelligent manufacturing. However, implementation challenges of scheduling algorithms in the context of smart manufacturing are not yet comprehensively studied. The purpose of this study is to show the scheduling No.s that need to be considered in the smart manufacturing paradigm. To attain this objective, the literature review is conducted in five stages using publish or perish tools from different sources such as Scopus, Pubmed, Crossref, and Google Scholar. As a result, the first contribution of this study is a critical analysis of existing production scheduling algorithms' characteristics and limitations from the viewpoint of smart manufacturing. The other contribution is to suggest the best strategies for selecting scheduling algorithms in a real-world scenario.

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“…For example, machine breakdown may happen at any time. So, it is also necessary to consider a maintenance plan along with real-time scheduling [34].…”

Section: Fig 2 Smart Manufacturing Modulesmentioning

confidence: 99%

Section: Fig 2 Smart Manufacturing Modulesmentioning

confidence: 99%

Scheduling Algorithms: Challenges Towards Smart Manufacturing

Workneh

Gmira

2022

Int. j. electr. comput. eng. syst. (Online)

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show abstract

“…In addition, Li et al 20 also considered the cost of total time occupied by machines in flow shop scheduling and took it as the optimization objective, proposed a balance method with the optimization objective of makespan, and applied this model to the actual situation of doctors' treatment, and achieved good results. Considering cost active adoption of dynamic scheduling and predictive scheduling is also an extension of FSP when machine is failure and maintenance during the production, and some achievements have been made in the related literature 21 – 23 research.…”

Section: Introductionmentioning

confidence: 99%

A computational efficient optimization of flow shop scheduling problems

Liang

Zhong

Liu

et al. 2022

Sci Rep

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Flow shop scheduling problems are NP-hard problems. Heuristic algorithms and evolutionary metaheuristic algorithms are commonly used to solve this kind of problem. Although heuristic algorithms have high solving speed, the solution quality is not good. Evolutionary algorithms make up for this defect in small-scale problems, but the solution performance will deteriorate with the expansion of the problem scale and there will be premature problems. In order to improve the solving accuracy of flow shop scheduling problems, a computational efficient optimization approach combining NEH and niche genetic algorithm (NEH-NGA) is developed. It is strengthened in the following three aspects: NEH algorithm is used to optimize the initial population, three crossover operators are used to enhance the genetic efficiency, and the niche mechanism is used to control the population distribution. A concrete application scheme of the proposed method is introduced. The results of compared with NEH heuristic algorithm and standard genetic algorithm (SGA) evolutionary metaheuristic algorithm after testing on 101 FSP benchmark instances show that the solution accuracy has been significantly improved.

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“…This method improved the convergence of the algorithm. Ye et al 50 proposed an adaptive strategy that introduced a correction factor to dynamically update the planned preventive maintenance interval. The strategy can achieve better results with lower total cost.…”

Section: Introductionmentioning

confidence: 99%

An adaptive immune‐following algorithm for intelligent optimal schedule of multiregional agricultural machinery

Jiang

Yang

et al. 2022

Int J of Intelligent Sys

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Aiming at low efficiency of agricultural machinery scheduling, this paper proposes an adaptive immune-following algorithm (AIFA) based on immune algorithm and artificial fish swarm algorithm. The adaptive crossover operator is used to accelerate convergence, and adaptive mutation operator ensures good diversity of population. After the adaptive evolution operations are performed, the following operator based on the following behavior of artificial fish swarm algorithm is embedded into the algorithm, which improves the convergence precision and obtains the promising optimization results. Experiments on scheduling considering the breakdown of agricultural machinery are performed based on multiple regions and multiple agricultural machineries. Compared with the immune algorithm and genetic algorithm, the simulation results demonstrate that AIFA can converge faster and achieve a better optimal solution.

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Adaptive Preventive Maintenance for Flow Shop Scheduling With Resumable Processing

Cited by 24 publications

References 43 publications

Scheduling Algorithms: Challenges Towards Smart Manufacturing

Scheduling Algorithms: Challenges Towards Smart Manufacturing

A computational efficient optimization of flow shop scheduling problems

An adaptive immune‐following algorithm for intelligent optimal schedule of multiregional agricultural machinery

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