Research on Dynamic Adaptation of Supply and Demand of Power Emergency Materials based on Cohesive Hierarchical Clustering

Xinju Zhang ()
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Xinju Zhang: Ministry of Emergency Management Big Data Center, Beijing, China

Innovation & Technology Advances, 2024, vol. 2, issue 2, 59-75

Abstract: Taking the dynamic adaptation of supply and demand of emergency materials as the research object, the condensed analytic hierarchy process was used to analyze the supply and demand risk factors in the supply chain of emergency materials from the supply side and the demand side. A new two-objective optimization model based on cohesive hierarchical clustering and electricity is proposed. Three distribution schemes are developed based on customer demand, shortest transportation time and the combination of both. When the overall satisfaction of the supply chain is the highest, the total revenue is maximized, which is suitable for the optimization of the supply chain of emergency materials under emergencies. Firstly, the power data and behavior data of power users are cross-analyzed to obtain the typical data characteristics of power users. Secondly, the cohesive hierarchical clustering algorithm is used to analyze the power users, and the cohesive hierarchical clustering algorithm is used to improve the calculation operation of score similarity. The adjustment factor is introduced to improve the calculation operation, and the comprehensive weight of each emergency point is determined as the parameter of the objective function by considering the subjective and objective factors that affect the importance of emergency points. Finally, through the simulation analysis of an emergency materials scheduling based on electric power, and compared with the results of random scheduling, the science and effectiveness of the proposed model are verified.

Keywords: Cohesive hierarchical clustering; Emergency supplies; Dynamic adaptation of supply and demand; Regulatory factor; Comprehensive weight of emergency point (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:cwi:itadva:v:2:y:2024:i:2:p:59-75

DOI: 10.61187/ita.v2i2.135

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