Uncertainty Spread and Control of Complex Supply Chain Networks in Manufacturing Industry

Qin, Xiao Hong; Luo, Bing Neng; Gong, Jun Quan

doi:10.4028/www.scientific.net/amr.712-715.3161

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…e connected edges include a single route and multiple route [24]. Single disaster route refers to factor A causing factor B to change, and thus, factor B can cause factor C to change.…”

Section: Composition Of the Complex Network Modelmentioning

confidence: 99%

Risk Early Warning Evaluation of Coal Mine Water Inrush Based on Complex Network and Its Application

Bai

Yan

et al. 2021

Advances in Civil Engineering

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As one of the five major coal mine disasters, the water inrush disaster poses a serious threat to the safety of the country and people, so the prevention work for that becomes very important. However, there is no perfect assessment system that can better solve the complex dependence relationships among disaster-causing factors of water inrush disasters. This study applied the knowledge of Complex Networks to research water inrush disaster, and based on that, the early warning evaluation system that combined ANP and Cloud model was established in order to solve the complex dependence problem and prevent the occurrence of water inrush. Moreover, this evaluation model was applied to the example Y coal mine to verify its superiority and feasibility. The results showed that the main cloud of goal was located at the yellow-strong warning level, and the first-level indicators were, respectively, at that the yellow-strong level of mining conditions, the yellow-strong warning level of hydrological factors, between the yellow-strong warning level and purple-general level of the geological structure, and among the blue-slightly weak warning level, purple-general level, and yellow-strong level of the human factor. The prediction results were consistent with the actual situation of the coal water inrush disaster in Y mine, which further proved that this early warning evaluation model is reliable. In response to the forecast results, the authors put forward relative improvements necessary to strengthen the prevention ability to disaster-causing factors among hydrological factors, mining conditions, and geological structure, which should comprehensively increase knowledge, technology, and management of workers to avoid leaving out disaster-causing factors. Meanwhile, the warning evaluation model also provides the relevant experience basis for other types of early warning assessment networks.

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“…e connected edges include a single route and multiple route [24]. Single disaster route refers to factor A causing factor B to change, and thus, factor B can cause factor C to change.…”

Section: Composition Of the Complex Network Modelmentioning

confidence: 99%

Risk Early Warning Evaluation of Coal Mine Water Inrush Based on Complex Network and Its Application

Bai

Yan

et al. 2021

Advances in Civil Engineering

View full text Add to dashboard Cite

show abstract

“…discuss various uncertainties of supply chain networks in manufacturing industry, and analyze the spread and control of uncertainty of complex supply chain network in manufacturing industry [124].…”

Section: Supply Chainmentioning

confidence: 99%