Research on Assignment Model and Optimization of Medical Waste Collection and Transportation Personnel Based on Task Equilibrium.

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]