scholarly journals A Data-Driven Residential Transformer Overloading Risk Assessment Method

Author(s):  
Ming Dong ◽  
Alexandre Nassif
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yong Peng ◽  
Yi Juan Luo ◽  
Pei Jiang ◽  
Peng Cheng Yong

PurposeDistribution of long-haul goods could be managed via multimodal transportation networks where decision-maker has to consider these factors including the uncertainty of transportation time and cost, the timetable limitation of selected modes and the storage cost incurred in advance or delay arriving of the goods. Considering the above factors comprehensively, this paper establishes a multimodal multi-objective route optimization model which aims to minimize total transportation duration and cost. This study could be used as a reference for decision-maker to transportation plans.Design/methodology/approachMonte Carlo (MC) simulation is introduced to deal with transportation uncertainty and the NSGA-II algorithm with an external archival elite retention strategy is designed. An efficient transformation method based on data-drive to overcome the high time-consuming problem brought by MC simulation. Other contribution of this study is developed a scheme risk assessment method for the non-absolutely optimal Pareto frontier solution set obtained by the NSGA-II algorithm.FindingsNumerical examples verify the effectiveness of the proposed algorithm as it is able to find a high-quality solution and the risk assessment method proposed in this paper can provide support for the route decision.Originality/valueThe impact of timetable on transportation duration is analyzed and making a detailed description in the mathematical model. The uncertain transportation duration and cost are represented by random number that obeys a certain distribution and designed NSGA-II with MC simulation to solve the proposed problem. The data-driven strategy is adopted to reduce the computational time caused by the combination of evolutionary algorithm and MC simulation. The elite retention strategy with external archiving is created to improve the quality of solutions. A risk assessment approach is proposed for the solution scheme and in the numerical simulation experiment.


2020 ◽  
Vol 34 (5) ◽  
pp. 627-640 ◽  
Author(s):  
Shi Xianwu ◽  
Qiu Jufei ◽  
Chen Bingrui ◽  
Zhang Xiaojie ◽  
Guo Haoshuang ◽  
...  

Author(s):  
Zuzhen Ji ◽  
Dirk Pons ◽  
John Pearse

Successful implementation of Health and Safety (H&S) systems requires an effective mechanism to assess risk. Existing methods focus primarily on measuring the safety aspect; the risk of an accident is determined based on the product of severity of consequence and likelihood of the incident arising. The health component, i.e., chronic harm, is more difficult to assess. Partially, this is due to both consequences and the likelihood of health issues, which may be indeterminate. There is a need to develop a quantitative risk measurement for H&S risk management and with better representation for chronic health issues. The present paper has approached this from a different direction, by adopting a public health perspective of quality of life. We have then changed the risk assessment process to accommodate this. This was then applied to a case study. The case study showed that merely including the chronic harm scales appeared to be sufficient to elicit a more detailed consideration of hazards for chronic harm. This suggests that people are not insensitive to chronic harm hazards, but benefit from having a framework in which to communicate them. A method has been devised to harmonize safety and harm risk assessments. The result was a comprehensive risk assessment method with consideration of safety accidents and chronic health issues. This has the potential to benefit industry by making chronic harm more visible and hence more preventable.


2021 ◽  
Vol 420 ◽  
pp. 129893
Author(s):  
Zijian Liu ◽  
Wende Tian ◽  
Zhe Cui ◽  
Honglong Wei ◽  
Chuankun Li

2021 ◽  
Vol 102 ◽  
pp. 102134
Author(s):  
Junjiang He ◽  
Tao Li ◽  
Beibei Li ◽  
Xiaolong Lan ◽  
Zhiyong Li ◽  
...  

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