Optimization of household medical waste recycling logistics routes: Considering contamination risks DOI Creative Commons
Jihui Hu, Ying Zhang, Yanqiu Liu

et al.

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(10), P. e0311582 - e0311582

Published: Oct. 7, 2024

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

Language: Английский

Vulnerability of fresh agricultural products supply chain: Assessment, interrelationship analysis and control strategies DOI
M.F. Yang, Shaojian Qu, Ying Ji

et al.

Socio-Economic Planning Sciences, Journal Year: 2024, Volume and Issue: 94, P. 101928 - 101928

Published: May 14, 2024

Language: Английский

Citations

1
Optimization of household medical waste recycling logistics routes: Considering contamination risks DOI Creative Commons
Jihui Hu, Ying Zhang, Yanqiu Liu

et al.

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(10), P. e0311582 - e0311582

Published: Oct. 7, 2024

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

Language: Английский

Citations

0