ESG (Environmental, Social and Governance) management practice is an important part of promoting sustainable operation and development of manufacturing enterprises. Currently, traditional evaluation methods have limit...
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ESG (Environmental, Social and Governance) management practice is an important part of promoting sustainable operation and development of manufacturing enterprises. Currently, traditional evaluation methods have limitations such as low efficiency and lack of objectivity. To improve the efficiency and accuracy of ESG evaluation and promote the optimization of ESG performance in manufacturing enterprises, this article combined data mining and analytic hierarchy process (AHP) to conduct effective research on ESG management practice evaluation in manufacturing enterprises. This article adopted the best priority search strategy to collect and process enterprise ESG data. By using AHP to construct hierarchical and segmented objectives for target problems, a performance evaluation index system for management practices was built based on the evaluation objectives and hierarchical priority order. Finally, based on the performance evaluation of ESG management practices, the K-nearest Neighbor algorithm was applied to analyze historical data of key indicators. According to the weights, various key indicators were re-integrated, achieving practical evaluation and decision support for enterprise ESG management. To verify the effectiveness of data mining and AHP, this article took Z enterprise as the research object and conducted empirical analysis on it. The results showed that in terms of evaluation accuracy, the method proposed in this article achieved the highest evaluation accuracy of 92.51%, 91.16%, and 91.75% in environmental, social, and governance dimension data use case evaluation, respectively. The conclusion indicated that data mining and AHP could improve the accuracy of ESG management practice evaluation in enterprises, provide reliable decision support for enterprise development, and help promote sustainable development of enterprises.
Traveling Salesman Problem is one of typical NP-hard problems of combinatorial *** is because of the complexity of TSP that accurate computing algorithms couldn't find a global optimal solution in more short time ...
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Traveling Salesman Problem is one of typical NP-hard problems of combinatorial *** is because of the complexity of TSP that accurate computing algorithms couldn't find a global optimal solution in more short time or at *** analyzing the relationship between global optimal solutions and local optimal solutions computed using heuristic algorithms for TSP, it is found that union set of edge sets of multi high-qualify local optimal solutions can include all of edges of a global optimal *** method, reducing initial edge set for TSP, is put forward based on probability statistic *** search space of original problem is cut down greatly by utilizing new method;the quantity of new initial edge set is about double times of problem *** computing algorithms can find global optimal solution for small scale TSP based on new edge sets, and efficiency of stochastic search algorithms is improved greatly.
Traditional scheduling strategies base their goals on maximizing the total system throughput. However, in recent research, the maximized total throughput does not necessarily represent the optimal system resource allo...
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ISBN:
(纸本)078039335X
Traditional scheduling strategies base their goals on maximizing the total system throughput. However, in recent research, the maximized total throughput does not necessarily represent the optimal system resource allocation. In this paper, we propose the Proportional Gradient Satisfaction Strategy (PGSS), which adds user satisfaction value to the existing scheduling criteria. PGSS schedules bandwidth in proportion to the temporal satisfaction gradient rather than original bandwidths used in traditional scheduling strategies. Moreover, PGSS substitutes user satisfaction value with its gradient to simplify the nonlinear problem of satisfaction value,traditionally solved by intelligence algorithms, in order to reduce the algorithm complexity to make PGSS practical to implement in a base station. Simulation shows that compared with proportional compensation algorithm, PGSS promotes the system aggregated satisfaction value and still guarantees the maximized system throughout.
作者：
Ran HaoDelin LuoHaibin DuanSchool of Reliability and Systems Engineering
Beihang UniversityBeijing100191 China Department of AutomationXiamen UniversityXiamenChina Science and Technology on Aircraft Control LaboratorySchool of Automation Science and Electrical EngineeringBeihang UniversityBeijingChina
Unmanned aerial vehicles (UAVs) have shown their superiority for applications in performing military and civilian *** which,multiple UAVs mission assignment is becoming more important for today's military *** ...
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ISBN:
(纸本)9781479946983
Unmanned aerial vehicles (UAVs) have shown their superiority for applications in performing military and civilian *** which,multiple UAVs mission assignment is becoming more important for today's military *** far,there have been many bio-inspired computation algorithms for solving multiple UAVs mission assignment problems,including particle swarm optimization (PSO),differential evolution algorithm (DE) and so ***,deficiencies of these approaches exist inevitably,which cannot satisfy the requirements of dynamic mission *** this paper,a new UAV assignment model focusing on the energy consumption of UAV is brought up which can be easily applied to intelligence ***,we propose a new approach by applying the modified Pigeon-Inspired Optimization (PIO) algorithm to sovle the multiple UAVs mission assignment *** simulation results show that the modified PIO algorithm is more effective when compared with other state-of-the-art algorithms in addressing mission assignment problem for multiple UAVs.
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