Abstract:

In order to improve the safety of medical waste recycling during pandemics, optimizing facility location, transportation routes and flow allocation were simultaneously considered. Firstly, according to the environmental transmission characteristics of infectious viruses, the nonlinear three-dimensional robust model was formulated for public perceived risks assessment. Secondly, with the minimization of total risk and cost, the robust scenario parameters of public perceived risk were designed, and the 0-1 mixed integer multi-objective nonlinear robust model was developed. And then, according to the characteristics of the proposed model's complexity, the two-phased solution procedure based on the “minimum envelope clustering-Generic algorithm” was also designed. Finally, several examples, including the real-life problem in Wuhan and several extended tests, were provided to demonstrate the workability of the model and algorithm. The computational results show that the proposed model and method provide stable performance under different conditions. The optimal plan with GAP of 0.86% could be obtained within 890 s. The new model and algorithm had strong parameter sensitivity and parameter sensitivity for the maximum capacity, fixed construction cost of the temporary transfer station, and traffic flow coefficient. Compared with the traditional model, the new model could provide an optimal plan with a reduction of 80.58% and 55.48% individually in total risk and average personal risk. The optimum results can be obtained by the proposed method with a reduction of 26.83% in total computational time. When solving larger scale optimization problems, it could increase the solution time by 22.25% to ensure the GAP value within 5%.

Key words: perceived risk, robust optimization, medical wastes, recycling system, multi-objective