Abstract

In order to build a more comprehensive emergency disposal and transportation network system for medical waste, it is necessary to consider various uncertain factors and data characteristics. Therefore, in the context of intelligent transportation, this article considers the uncertainty of the quantity and regional population density of infectious medical waste generation as well as the emergency disposal of infectious medical waste under multi-cycle and multi-objective conditions, and it constructs a multi-cycle emergency disposal logistics network optimization model for infectious medical waste under uncertain conditions. Through deterministic transformation of the model and data mining of the medical waste disposal logistics network in Wuhan, China, the multi-objective model under uncertain conditions was also solved and sensitivity analyzed using the MOPSO-NSGA2 intelligent algorithm, verifying the effectiveness and superiority of the algorithm.

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