关键词: 疫情暴发流行期, 传染性医疗废弃物, 中转点, 处理中心, 多目标优化模型

Abstract: COVID-19 has been the sixth global public health emergency so far, with a wide transmission range, high infectivity and multiple transmission modes, making hospital infection control difficult. During the epidemic outbreak period, the discharge of the infectious medical waste increases rapidly. Hospitals, medical waste transfer stations and medical waste treatment centers are facing multiple pressures of waste collection, transportation and treatment, as well as serious impacts on human health and social environment. Strengthening the medical waste management, providing advance warning, and improving medical waste disposal capabilities can effectively curb the epidemic spread and reduce environmental pressures. It is necessary to optimize the transport network between hospitals, transfer stations, and treatment centers, reduce the socioeconomic stress and social infection risk, and improve the transportation efficiency. For the past years, the problem of transportation routes selection for infectious medical waste has attracted scholars’ attention, and some research results have been achieved. However, there is a lack of study that integrates effects of the social infection risk, transportation efficiency and socioeconomic stress. Therefore, optimizing the transport network of infectious medical waste based on the actual situation during the COVID-19 pandemic, and ensuring the safety, timeliness and economy of the collection and transportation process, are realistic research problems that need to be addressed.
The problem concerned in this paper is how to transfer the infectious medical waste generated in hospitals to transfer stations as soon as possible, and then to treatment centers under certain constraints. To make it more concrete, this paper proposes a multi-objective three-level transport network optimization model in order to effectively solve the problem. The multi-objective three-level transport network optimization model of infectious medical waste involves four major steps: (1)Giving the function of time satisfaction to avoid waste accumulation. (2)Establishing a cost optimization function to relieve socioeconomic stress, such as transportation cost, sterilizing cost, disposal cost, etc. (3)Adopting the prospect theory to assess the anti-risk capability of medical waste treatment centers. (4)Based on the time satisfaction function, cost optimization function and anti-risk capability evaluation results, the multi-objective optimization model is established, and the appropriate transfer stations and treatment centers are selected. To transform them into a single objective optimization model, the multi-objective problem is resolved by the weighting method. Finally, an illustrative example is solved using optimizing software packages to verify the method’s feasibility and effectiveness.
Several main conclusions are drawn: (1)The research subject has typical characteristics such as infectivity, which is a relatively novel research field. (2)The paper is analyzed from a systematic and global perspective rather than a single perspective. The research subject is a three-level transportation network and the research objective comprehensively considers the safety, timeliness and economy, which makes results more in line with the actual behavior of decision makers. (3)The characteristic of decision makers’ bounded rationality is considered in the model. (4)The sensitivity analysis of important parameters in the example shows that the transport network during the pandemic may have been influenced by contagious characteristics. Furthermore, there are still many fields for future researches. For example, monetizing the infection risk during the pandemic is an important reference for quantitatively measuring the level of waste risk management. The data size can also be expanded and a variety of intelligent optimization algorithms can be introduced, so as to accelerate the solving speed and the results’ accuracy.

Key words: epidemic period, infectious medical waste, transfer stations, processing centers, multi-objective optimization model