Artificial intelligence

Abstract: In the context of enhancing medical safety systems, AI-based optimization of packing solutions is crucial, particularly in the secure and sanitary storage of hazardous materials within healthcare environments. Ensuring that toxic, radioactive, or infectious substances are stored according to strict sanitary guidelines is vital for protecting public health and preventing contamination. This study introduces an AI-driven mathematical model to address packing problems, with a focus on meeting both technological and hygiene standards. By modeling the optimal placement of containers in a deployment area and incorporating key safety and sanitary restrictions, the problem is reduced to a nonlinear programming framework. The phi-function technique is utilized to capture geometric relationships effectively, enabling the development of AI-optimized, sanitary-compliant storage solutions. Numerical examples are provided to demonstrate the approach's efficacy.

Keywords: AI-based optimization, medical safety, packing problem, safety clearance, deployment area, mathematical modelling, non-linear programming

References:

1. Shah, I.A., Mishra, S.D. (2024) Artificial intelligence in advancing occupational health and safety: an encapsulation of developments, Journal of Occupational Health, 66(1), 1–12. doi: 10.1093/joccuh/uiad017.
2. Andeobu, L. (2023) Medical Waste and Its Management. In: Brinkmann, R. (eds) The Palgrave Handbook of Global Sustainability. Palgrave Macmillan, Cham, pp. 761-789. doi: 10.1007/978-3-031-01949-4_53.
3. Bernabeu-Martínez, M.A., Ramos Merino, M, Santos Gago, J.M., Álvarez Sabucedo, L,M,, Wanden-Berghe, C., et al. (2018) Guidelines for safe handling of hazardous drugs: A systematic review. PLOS ONE, 13(5), e0197172. doi: 10.1371/journal.pone.0197172.
4. Attrah, M., Elmanadely, A., Akter, D., Rene, E.R. (2022) A Review on Medical Waste Management: Treatment, Recycling, and Disposal Options. Environments, 9, 146. doi: 10.3390/environments9110146 .
5. Romanova, T.E., Stetsyuk, P.I., Chugay, A.M. et al. (2019) Parallel Computing Technologies for Solving Optimization Problems of Geometric Design. Cybern Syst Anal. 55, 894–904. doi: 10.1007/s10559-019-00199-4.
6. Kallrath, J. (2021) Cutting and Packing Beyond and Within Mathematical Programming. In: Business Optimization Using Mathematical Programming. International Series in Operations Research & Management Science, vol 307. Springer, Cham. doi: 10.1007/978-3-030-73237-0_15.
7. Chuhai, A., Starkova, O., Yaskova, Y. (2023) Intelligent System for Radio-Surgical Treatment Planning Using Spheres Placement Optimization with Overlaps. Stuc. intelekt., 28(3), 104–108. doi: 10.15407/jai2023.03.104.
8. Chuhai, A.M., Yaskova, Y.G., Dubinskyi, V.M. (2022) An Intelligent Decision Support System for Solving Optimized Geometric Design Problems. Stuc. intelekt., 27(2), 29–37. doi: 10.15407/jai2022.02.029.
9. Litvinchev, I., Infante, L., Ozuna, L. (2015) Packing circular-like objects in a rectangular container. J. Comput. Syst. Sci. Int., 54, 259–267. doi: 10.1134/S1064230715020070.
10. He, K., Ye, H., Wang, Z. Liu, J. (2017) An Efficient Quasi-physical Quasi-human Algorithm for Packing Equal Circles in a Circular Container. arXiv:1611.02323. doi:10.48550/arXiv.1611.02323.
11. Peralta, J., Andretta, M., Oliveira, J. F. (2017) Solving Irregular Strip Packing Problems With Free Rotations Using Separation Lines. arXiv: 1707.07177. doi: 10.48550/arXiv.1707.07177.
12. Stoyan, Y., Pankratov, A., Romanova, T. (2016) Cutting and packing problems for irregular objects with continuous rotations: mathematical modelling and non-linear optimization. The Journal of the Operational Research Society, 67(5), 786–800. http://www.jstor.org/stable/44988093.
13. Waechter, A., Biegler, L.T. (2006) On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Math. Program., 106, 25–57. doi: 10.1007/s10107-004-0559-y.