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During major epidemic outbreaks, the generation of medical waste has increased dramatically. Medical waste becomes more infectious than usual under these circumstances, and turnover personnel may be affected or become ill due to infection. These unforeseen factors have significantly increased the complexity of task assignment for medical waste turnover personnel. In addition, medical waste requires daily collection and disposal. To ensure the smooth collection of medical waste, the reasonable arrangement of turnover personnel is particularly important. It is well known that the fewer times medical waste turnover personnel are assigned to different temporary storage points for medical waste, the less likely cross-infection occurs. In this paper, aiming to minimize the number of assignments and the number of medical waste turnover personnel assigned, while ensuring a balanced collection task for the assigned medical waste turnover personnel, a multi-objective medical waste turnover personnel assignment model is established, aiming to minimize the number of assignments while ensuring a balanced workload among the assigned personnel. To facilitate solving, this paper converts nonlinear constraints in the model into linear constraints using the Big M method. Finally, based on the medical waste collection and disposal data of a medical waste disposal unit in a certain district of Chengdu city on April 13, 2022, relevant numerical examples are designed and solved using IBM ILOG Cplex Studio IDE 12.7.1, thereby verifying the efficiency and applicability of the model. Our model is designed to provide a relatively rational personnel scheduling plan under the context of major epidemic outbreaks, where the uncertainty for medical waste collection and transportation personnel is heightened. This model aims to minimize the number of personnel required for medical waste collection and transportation while ensuring a balanced workload distribution among them. In comparison to manual scheduling methods, our model significantly enhances the efficiency of scheduling. [ABSTRACT FROM AUTHOR]

At present, the recycling rate of End-of-life vehicles (ELVs) in China is far lower than the heavily motorized countries, resulting in severe environmental pollution, waste of resource and hidden traffic troubles, which has a negative impact on China's economic and social sustainable development. With the arrival of the peak season of generating ELVs in China, it is urgent and important to improve the recycling rate of ELVs. The recycling rate of ELVs is influenced by multiple factors together. The existing research about the identification of influence factors of ELV recycling rate fewer considers the interaction effect among different factors. To fill the gap, firstly the influence factors are analyzed from standpoints of economy, policy, recycling network and others. Then a hybrid model based on DEMATEL (decision making trial and evaluation laboratory) and intuitionistic fuzzy sets is employed to examine the cause-effect relationships among factors. In the study, the vagueness of decision makers' judgment and linguistic inaccuracy is dealt with effectively by intuitionistic fuzzy sets. The research results reveal "fiscal subsidy", "government's restrictive policies to reuse ELVs parts", "coordination of the industry agencies", "participation of automobile manufacturer" and "supervision of the government" are most significant criteria influencing the recycling rate of ELVs in China. A sensitivity analysis is conducted to verify the robustness of results. Lastly, according to the critical influencing factors identified, some pertinent suggestions to the government, ELV recycling enterprise, owner and vehicle manufacturer are put forward. In addition, the future research directions are proposed. [ABSTRACT FROM AUTHOR]