Abstract
The medical waste transportation plays an important role in controlling pandemic, however, the explosive increase in medical waste (MW) makes it difficult to make optimal decisions. In this study, a two-stage optimization model considering the transshipment strategy is developed to transport MW in pandemic. In the first stage, a transit model is established to determine the transshipment relationship between medical waste generation nodes (MWN). In the second stage, a multi-trip and split-collection vehicle routing problem with collection time (MTSCVRPCT) model is established for MW collection with the objectives of minimizing the maximum total trip duration and minimizing the total transport risk. The transit model is a nonlinear integer problem and MTSCVRPCT model is a mixed integer programming problem, thus, we apply CPLEX solver to solve the transit model and use Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to solve the MTSCVRPCT model. A case study based on the real data in Shenzhen is conducted to demonstrate the workability of the proposed model. Compared with established transportation way, results show that the transshipment strategy can significantly reduce transportation time and risks.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (Grants Nos. 71901152), Guangdong Basic and Applied Basic Research Foundation (2023A1515010919), Guangdong Innovation Team Project of Intelligent Management and Cross Innovation (2021WCXTD002), Shenzhen Stable Support Grant (Grant Nos. 20220810100952001), and Shenzhen University-Lingnan University Joint Research Programme.
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Wang, H., Zhang, X., Zhang, J., Niu, B. (2024). A Two-Stage Approach to Optimize Medical Waste Transportation Problem During Pandemic. In: Tan, Y., Shi, Y. (eds) Data Mining and Big Data. DMBD 2023. Communications in Computer and Information Science, vol 2018. Springer, Singapore. https://doi.org/10.1007/978-981-97-0844-4_19
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DOI: https://doi.org/10.1007/978-981-97-0844-4_19
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