The Design of Medical Waste Recycling Network Based on Location and Path Collaborative Optimization

Yingpeng Xue

doi:10.54097/xh3dz090

Authors

Yingpeng Xue

DOI:

https://doi.org/10.54097/xh3dz090

Keywords:

Medical Waste Recycling, Dual-Layer Optimization Model, Reliability Analysis, Harmony Search Genetic Algorithm

Abstract

This thesis designs and optimizes a medical waste recycling network for enhanced efficiency and reduced environmental/social impacts. Addressing China's surge in waste volume, it introduces a dual-layer optimization model optimizing both collection point selection and transportation routing. The model incorporates innovative "loading reliability" and "travel time reliability" constraints to handle uncertainty. A hybrid algorithm (Simulated Annealing + Improved Harmony Search Genetic Algorithm) solves the problem, optimizing facility location and time-constrained vehicle routes. Case study in Shanghai demonstrated the model's effectiveness. Joint optimization outperformed single optimizations, reducing total costs and showing coordination importance. Improved reliability was found to reduce overload and delay risks, optimizing system performance. The research provides theoretical support and practical solutions for robust and economical medical waste management.

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References

[1] Cagliano A C, Pilloni M T, Rafele C. A multi-criteria fuzzy method for selecting the location of a solid waste disposal facility[J]. International Journal of Management and Decision Making, 2014, 13(3): 221-249.

[2] Kargar S, Pourmehdi M, Paydar M M. Reverse logistics network design for medical waste management in the epidemic outbreak of the novel coronavirus (COVID-19)[J]. Science of the Total Environment, 2020, 746: 141183.

[3] Yao L, Xu Z, Zeng Z. A soft‐path solution to risk reduction by modeling medical waste disposal center location‐allocation optimization[J]. Risk Analysis, 2020, 40(9): 1863-1886.

[4] Taslimi M, Batta R, Kwon C. Medical waste collection considering transportation and storage risk[J]. Computers & Operations Research, 2020, 120: 104966.

[5] Govindan K, Nosrati-Abarghooee S, Nasiri M M, et al. Green reverse logistics network design for medical waste management: A circular economy transition through case approach[J]. Journal of Environmental Management, 2022, 322: 115888.

[6] Šomplák R, Kropáč J, Pluskal J, et al. A Multi-Commodity Mathematical Modelling Approach—Hazardous Waste Treatment Infrastructure Planning in the Czech Republic[J]. Sustainability, 2022, 14(6): 3536.

[7] Abu-Qdais H A, Al-Ghazo M A, Al-Ghazo E M. Statistical analysis and characteristics of hospital medical waste under novel Coronavirus outbreak[J]. Global Journal of Environmental Science and Management, 2020, 6(4): 1-10.

[8] Wang W, Osaragi T. Lognormal distribution of daily travel time and a utility model for its emergence[J]. Transportation research part A: policy and practice, 2024, 183: 104058.

[9] Mor B, Shabtay D, Yedidsion L. Heuristic algorithms for solving a set of NP-hard single-machine scheduling problems with resource-dependent processing times[J]. Computers & Industrial Engineering, 2021, 153: 107024.

[10] Kirkpatrick S, Gelatt Jr C D, Vecchi M P. Optimization by simulated annealing[J]. science, 1983, 220(4598): 671-680.

[11] Elshaer R, Awad H. A taxonomic review of metaheuristic algorithms for solving the vehicle routing problem and its variants[J]. Computers & Industrial Engineering, 2020, 140: 106242.

[12] Ballester P J, Stephenson J, Carter J N, et al. Real-parameter optimization performance study on the CEC-2005 benchmark with SPC-PNX[C]//2005 IEEE Congress on Evolutionary Computation. IEEE, 2005, 1: 498-505.