School of Management, Shenyang University of Technology, Shenyang, Liaoning, China.
PLoS One. 2024 Oct 7;19(10):e0311582. doi: 10.1371/journal.pone.0311582. eCollection 2024.
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.
家庭医疗废物的产生量不断增加，这是工业化和全球人口增长的副产品，其运输和物流管理已成为一个重大的社会关注点。本研究深入探讨了家庭医疗废物物流网络中车辆路线的优化问题，这是对有效管理这种废物的必要回应。由于废物处理不当而造成的环境和公共卫生危害的潜在风险，促使我们将污染风险（受运输持续时间、废物量和风速的影响）纳入分析。为了增强模拟的现实性，将交通拥堵纳入车辆速度函数中，反映城市道路的可变性。随后，制定了一个双目标混合整数规划模型，以同时最小化总运营成本和环境污染风险。优化问题的复杂性促使我们开发了具有非支配排序的自适应混合人工鱼群算法（AH-NSAFSA）。该算法采用了一种复杂的方法，将拥挤距离和个体排名合并起来，从种群中识别出最佳解决方案。它还包含一个衰减函数，以促进自适应迭代过程，提高算法的收敛性能。此外，它利用交叉诱导消除的概念来保持遗传多样性和解决方案集的整体鲁棒性。使用 Solomon 数据集派生的测试集对 AH-NSAFSA 进行了实证评估，结果表明该算法能够为家庭医疗废物回收路径规划生成可行的非支配解。通过与非支配排序人工鱼群算法（NSAFSA）和非支配排序遗传算法 II（NSGA-II）的比较分析，在 MID、SM、NOS 和 CT 等指标上，AH-NSAFSA 在 MID、SM 和 NOS 方面表现出色，在 CT 方面优于 NSAFSA，但相对 NSGA-II 略逊一筹。该研究采用整体方法进行废物回收路径规划，将成本效益与污染风险和交通拥堵考虑因素相结合，为企业制定可持续绿色发展战略提供了有力支持。AH-NSAFSA 为医疗废物回收提供了一种生态效益和整体性的方法，推进了可持续管理实践。
