Sustainable development-oriented location-transportation integrated optimization problem regarding multi-period multi-type disaster medical waste during COVID-19 pandemic

Research on transportation management model of COVID-19 medical waste: a case study in Beijing, China

During the COVID-19 pandemic, disposable masks, protective clothing, gloves, and nasopharyngeal swabs collected by nucleic acid testing formed a large amount of medical waste. Medical waste has strict temporary storage time requirements in hospitals, which need to be transported to medical waste disposal plants within the specified time. However, as most of disposal plants are far away from downtown, they also need to be responsible for the transportation and disposal of medical waste in many hospitals, and put forward higher requirement for transportation routes. Rapid and safe disposal of all types of medical waste generated by COVID-19 is crucial to the prevention and control of the epidemic. This paper designs the transportation route optimization model using Anylogic simulation software based on the regional distribution of 118 tertiary hospitals and 2 large medical waste disposal plants in Beijing, China. At the same time, transportation routes of 118 tertiary hospitals in the morning peak, evening peak, all-day, and ordinary periods were simulated based on the Beijing traffic index in 2017. On this basis, through the analysis of the simulation data, the selection of medical waste transport routes for 118 tertiary hospitals in the morning peak, evening peak, all day, and ordinary periods is further clarified, so as to ensure that medical waste can be transported to the medical waste disposal plant in the shortest time. The shortest path and fastest speed transport mode, medical waste transport data set, and the selection of transport mode of 118 tertiary hospitals formed by this research provide certain reference experience for the rapid and safe transport of medical waste during the epidemic period, and also provides corresponding data support for medical waste transportation management in the post-epidemic era and medical waste transportation decision-making when facing major public health problems.

The IoT-enabled sustainable reverse supply chain for COVID-19 Pandemic Wastes (CPW)

Supply chains have been impacted by the COVID-19 pandemic, which is the most recent worldwide disaster. After the world health organization recognized the latest phenomena as a pandemic, nations became incapacitated to provide the required medical supplies. In the current situation, the world seeks an essential solution for COVID-19 Pandemic Wastes (CPWs) by pushing the pandemic to a stable condition. In this study, the development of a supply chain network is contrived for CPWs utilizing optimization modeling tools. Also, an IoT platform is devised to enable the proposed model to retrieve real-time data from IoT devices and set them as the model's inputs. Moreover, sustainability aspects are appended to the proposed IoT-enabled model considering its triplet pillars as objective functions. A real case of Puebla city and 15 experiments are used to validate the model. Furthermore, a combination of metaheuristic algorithms utilized to solve the model and also seven evaluation indicators endorse the selection of efficient solution approaches. The evaluation indicators are appointed as the inputs of statistical and multicriteria decision-making hybridization to prioritize the algorithms. The result of the Entropy Weights method and Combined Compromise Solution approach confirms that MOGWO has better performance for the medium-sizes, case study and an overall view. Also, NSHHO outclasses the small-size and large-size experiments.

Two-phase COVID-19 medical waste transport optimisation considering sustainability and infection probability

A safe and effective medical waste transport network is beneficial to control the COVID-19 pandemic and at least decelerate the spread of novel coronavirus. Seldom studies concentrated on a two-phase COVID-19 medical waste transport in the presence of multi-type vehicle selection, sustainability, and infection probability, which is the focus of this paper. This paper aims to identify the priority of sustainable objectives and observe the impacts of multi-phase and infection probability on the results. Thus, such a problem is formulated as a mixed-integer programming model to minimise total potential infection risks, minimise total environmental risks, and maximise total economic benefits. Then, a hybrid solution strategy is designed, incorporating a lexicographic optimisation approach and a linear weighted sum method. A real-world case study from Chongqing is used to illustrate this methodology. Results indicate that the solution strategy guides a good COVID-19 medical waste transport scheme within 1 min. The priority of sustainable objectives is society, economy, and environment in the first and second phases because the total Gap of case No.35 is 3.20%. A decentralised decision mode is preferred to design a COVID-19 medical waste transport network at the province level. Whatever the infection probability is, infection risk is the most critical concern in the COVID-19 medical waste clean-up activities. Environmental and economic sustainability performance also should be considered when infection probability is more than a certain threshold.