The Experts below are selected from a list of 12591 Experts worldwide ranked by ideXlab platform
Chengbin Chu - One of the best experts on this subject based on the ideXlab platform.
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A multi-objective distribution-free model and method for stochastic disassembly line balancing problem
International Journal of Production Research, 2020Co-Authors: Feng Chu, Feifeng Zheng, Ming Liu, Chengbin ChuAbstract:End-of-life product recycling is a hot research topic in recent years, which can reduce the waste and protect the environment. To disassemble products, the disassembly line balancing is a principal problem that selects tasks and assigns them to a number of workstations under stochastic task processing times. In existing works, stochastic task processing times are usually estimated by probability distributions or Fuzzy numbers. However, in real-life applications, only their partial information is accessible. This paper studies a bi-objective stochastic disassembly line balancing problem to minimise the line design cost and the cycle time, with only the knowledge of the mean, standard deviation and upper bound of stochastic task processing times. For the problem, a bi-objective chance-constrained model is developed, which is further approximated into a bi-objective distribution-free one. Based on the problem analysis, two versions of the ϵ-constraint method are proposed to solve the transformed model. Finally, a Fuzzy-Logic Technique is adapted to propose a preferable solution for decision makers according to their preferences. A case study is presented to illustrate the validity of the proposed models and algorithms. Experimental results on 277 benchmark-based and randomly generated instances show the efficiency of the proposed methods.
Feng Chu - One of the best experts on this subject based on the ideXlab platform.
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A multi-objective distribution-free model and method for stochastic disassembly line balancing problem
International Journal of Production Research, 2020Co-Authors: Feng Chu, Feifeng Zheng, Ming Liu, Chengbin ChuAbstract:End-of-life product recycling is a hot research topic in recent years, which can reduce the waste and protect the environment. To disassemble products, the disassembly line balancing is a principal problem that selects tasks and assigns them to a number of workstations under stochastic task processing times. In existing works, stochastic task processing times are usually estimated by probability distributions or Fuzzy numbers. However, in real-life applications, only their partial information is accessible. This paper studies a bi-objective stochastic disassembly line balancing problem to minimise the line design cost and the cycle time, with only the knowledge of the mean, standard deviation and upper bound of stochastic task processing times. For the problem, a bi-objective chance-constrained model is developed, which is further approximated into a bi-objective distribution-free one. Based on the problem analysis, two versions of the ϵ-constraint method are proposed to solve the transformed model. Finally, a Fuzzy-Logic Technique is adapted to propose a preferable solution for decision makers according to their preferences. A case study is presented to illustrate the validity of the proposed models and algorithms. Experimental results on 277 benchmark-based and randomly generated instances show the efficiency of the proposed methods.
Ayele Almaw Fenta - One of the best experts on this subject based on the ideXlab platform.
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land susceptibility to water and wind erosion risks in the east africa region
Science of The Total Environment, 2020Co-Authors: Ayele Almaw Fenta, Atsushi Tsunekawa, Nigussie Haregeweyn, Jean Poesen, Mitsuru Tsubo, Pasquale Borrelli, Panos Panagos, Matthias Vanmaercke, Jente BroeckxAbstract:Abstract Land degradation by water and wind erosion is a serious problem worldwide. Despite the significant amount of research on this topic, quantifying these processes at large- or regional-scale remains difficult. Furthermore, very few studies provide integrated assessments of land susceptibility to both water and wind erosion. Therefore, this study investigated the spatial patterns of water and wind erosion risks, first separately and then combined, in the drought-prone region of East Africa using the best available datasets. As to water erosion, we adopted the spatially distributed version of the Revised Universal Soil Loss Equation and compared our estimates with plot-scale measurements and watershed sediment yield (SY) data. The order of magnitude of our soil loss estimates by water erosion is within the range of measured plot-scale data. Moreover, despite the fact that SY integrates different soil erosion and sediment deposition processes within watersheds, we observed a strong correlation of SY with our estimated soil loss rates (r2 = 0.4). For wind erosion, we developed a wind erosion index by integrating five relevant factors using Fuzzy Logic Technique. We compared this index with estimates of the frequency of dust storms, derived from long-term Sea-Viewing Wide Field-of-View Sensor Level-3 daily data. This comparison revealed an overall accuracy of 70%. According to our estimates, mean annual gross soil loss by water erosion amounts to 4 billion t, with a mean soil loss rate of 6.3 t ha−1 yr−1, of which ca. 50% was found to originate in Ethiopia. In terms of land cover, ca. 50% of the soil loss by water erosion originates from cropland (with a mean soil loss rate of 18.4 t ha−1 yr−1), which covers ca. 15% of the total area in the study region. Model results showed that nearly 10% of the East Africa region is subject to moderate or elevated water erosion risks (>10 t ha−1 yr−1). With respect to wind erosion, we estimated that around 25% of the study area is experiencing moderate or elevated wind erosion risks (equivalent to a frequency of dust storms >45 days yr−1), of which Sudan and Somalia (which are dominated by bare/sparse vegetation cover) have the largest share (ca. 90%). In total, an estimated 8 million ha is exposed to moderate or elevated risks of soil erosion by both water and wind. The results of this study provide new insights on the spatial patterns of water and wind erosion risks in East Africa and can be used to prioritize areas where further investigations are needed and where remedial actions should be implemented.
P Austin - One of the best experts on this subject based on the ideXlab platform.
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a Fuzzy dissolved gas analysis method for the diagnosis of multiple incipient faults in a transformer
IEEE Transactions on Power Systems, 2000Co-Authors: Q Su, C Mi, L L Lai, P AustinAbstract:Dissolved gas analysis (DGA) of transformer oil has been one of the most useful Techniques to detect the incipient faults. Various methods, such as the IEC codes, have been developed to interpret DGA results directly obtained from a chromatographer. Although these methods are widely used in the world, they sometimes fail to diagnose, especially when more than one fault exists in a transformer. This paper presents a Fuzzy Logic Technique which can diagnose multiple faults in a transformer and quantitatively indicates the likelihood/severity of each fault. Insulation deterioration at each fault location can then be monitored closely according to its trend, which is important for a transformer in critical situation. Tests using this Technique on a number of transformers have given promising results.
Feifeng Zheng - One of the best experts on this subject based on the ideXlab platform.
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A multi-objective distribution-free model and method for stochastic disassembly line balancing problem
International Journal of Production Research, 2020Co-Authors: Feng Chu, Feifeng Zheng, Ming Liu, Chengbin ChuAbstract:End-of-life product recycling is a hot research topic in recent years, which can reduce the waste and protect the environment. To disassemble products, the disassembly line balancing is a principal problem that selects tasks and assigns them to a number of workstations under stochastic task processing times. In existing works, stochastic task processing times are usually estimated by probability distributions or Fuzzy numbers. However, in real-life applications, only their partial information is accessible. This paper studies a bi-objective stochastic disassembly line balancing problem to minimise the line design cost and the cycle time, with only the knowledge of the mean, standard deviation and upper bound of stochastic task processing times. For the problem, a bi-objective chance-constrained model is developed, which is further approximated into a bi-objective distribution-free one. Based on the problem analysis, two versions of the ϵ-constraint method are proposed to solve the transformed model. Finally, a Fuzzy-Logic Technique is adapted to propose a preferable solution for decision makers according to their preferences. A case study is presented to illustrate the validity of the proposed models and algorithms. Experimental results on 277 benchmark-based and randomly generated instances show the efficiency of the proposed methods.
