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Optimization of household medical waste recycling logistics routes: Considering contamination risks

Author

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  • Jihui Hu
  • Ying Zhang
  • Yanqiu Liu
  • Jiaqi Hou
  • Aobei Zhang

Abstract

The escalating generation of household medical waste, a byproduct of industrialization and global population growth, has rendered its transportation and logistics management a critical societal concern. This study delves into the optimization of routes for vehicles within the household medical waste logistics network, a response to the imperative of managing this waste effectively. The potential for environmental and public health hazards due to improper waste disposal is acknowledged, prompting the incorporation of contamination risk, influenced by transport duration, waste volume, and wind velocity, into the analysis. To enhance the realism of the simulation, traffic congestion is integrated into the vehicle speed function, reflecting the urban roads’ variability. Subsequently, a Bi-objective mixed-integer programming model is formulated to concurrently minimize total operational costs and environmental pollution risks. The complexity inherent in the optimization problem has motivated the development of the Adaptive Hybrid Artificial Fish Swarming Algorithm with Non-Dominated Sorting (AH-NSAFSA). This algorithm employs a sophisticated approach, amalgamating congestion distance and individual ranking to discern optimal solutions from the population. It incorporates a decay function to facilitate an adaptive iterative process, enhancing the algorithm’s convergence properties. Furthermore, it leverages the concept of crossover-induced elimination to preserve the genetic diversity and overall robustness of the solution set. The empirical evaluation of AH-NSAFSA is conducted using a test set derived from the Solomon dataset, demonstrating the algorithm’s capability to generate feasible non-dominated solutions for household medical waste recycling path planning. Comparative analysis with the Non-dominated Sorted Artificial Fish Swarm Algorithm (NSAFSA) and Non-dominated Sorted Genetic Algorithm II (NSGA-II) across metrics such as MID, SM, NOS, and CT reveals that AH-NSAFSA excels in MID, SM, and NOS, and surpasses NSAFSA in CT, albeit slightly underperforming relative to NSGA-II. The study’s holistic approach to waste recycling route planning, which integrates cost-effectiveness with pollution risk and traffic congestion considerations, offers substantial support for enterprises in formulating sustainable green development strategies. AH-NSAFSA offers an eco-efficient, holistic approach to medical waste recycling, advancing sustainable management practices.

Suggested Citation

  • Jihui Hu & Ying Zhang & Yanqiu Liu & Jiaqi Hou & Aobei Zhang, 2024. "Optimization of household medical waste recycling logistics routes: Considering contamination risks," PLOS ONE, Public Library of Science, vol. 19(10), pages 1-26, October.
  • Handle: RePEc:plo:pone00:0311582
    DOI: 10.1371/journal.pone.0311582
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    References listed on IDEAS

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    1. Wanting Zhang & Ming Zeng & Peng Guo & Kun Wen, 2022. "Variable Neighborhood Search for Multi-Cycle Medical Waste Recycling Vehicle Routing Problem with Time Windows," IJERPH, MDPI, vol. 19(19), pages 1-25, October.
    2. Lan Zhu & Tao Ding & Zhuofan Liu, 2024. "Reverse Logistics Network Design for Medical Waste Disposal under the Scenario of Uncertain Proposal Demand," Sustainability, MDPI, vol. 16(7), pages 1-14, April.
    3. Xuan Luo & Wenzhu Liao, 2022. "Collaborative Reverse Logistics Network for Infectious Medical Waste Management during the COVID-19 Outbreak," IJERPH, MDPI, vol. 19(15), pages 1-28, August.
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