OVERVIEW AND ANALYSIS OF THREE-DIMENSIONAL PACKAGING FOR MARINE CARGO TRANSPORTATION
Cite as: V.V. Kureichik, Y.V. Balyasova, V.V. Bova. Overview and analysis of three-dimensional packaging for marine cargo transportation // Izvestiya SFedU. Engineering Sciences – 2024. – N. 6. - P. 15-29. doi: 10.18522/2311-3103-2024-6-15-29
Abstract
This article describes the problem of three-dimensional packaging of goods in various types of containers
during maritime cargo transportation. Maritime cargo transportation plays a significant role in
international trade, is carried out in specific and non-standard conditions, is characterized by increased
humidity, contact with sea salt, vibration, temperature interference and is carried out by container ships
transporting various categories of goods in containers selected taking into account the specifics of the
cargo being transported, which ensures reliability and safety. Of particular importance is the presence of
protection of goods from a variety of negative and man-made environmental factors, which confirms the
importance of properly designed marine cargo packaging, ensuring the preservation of goods, equipment,
raw materials, or materials throughout the entire time of transportation by sea, as well as reliable fastening
on deck or inside cargo compartments, excluding the possibility of damage to cargo, through exposure
vibration and static loads. The article describes the task of three-dimensional packaging in containers
for marine cargo transportation. Criteria and constraints are considered, and a modified combined
multi-criteria objective function is constructed. Its value should tend to 1, which corresponds to 100%
filling of voids. Also, the paper provides a brief overview and analysis of methods and algorithms for finding
solutions to the problem of three-dimensional packaging, their features, advantages and disadvantages
are revealed. Taking into account the analysis, it is noted that metaheuristic methods and search algorithms
are effective for solving the NP-complex problem of three-dimensional packaging, as they allow
obtaining sets of quasi-optimal solutions in polynomial time.
References
[GOST R 53350-2009 (ISO 668:1995) Series 1 freight containers. Classification, dimensions and
weight] (date of introduction 2010-01-01)].
2. GOST 26653-2015 Podgotovka general'nykh gruzov k transportirovaniyu. Obshchie trebovaniya
[GOST 26653-2015 Preparation of general cargo for transportation. General requirements] (date of introduction
2017-03-01).
3. ISPM 15 Fitosanitarnyy standart [ISPM 15 Phytosanitary standard] (date of introduction 2002-03).
4. Kureychik V.V., Glushchenko A.E., Orlov A.N. Gibridnyy podkhod dlya resheniya zadachi 3-khmernoy
upakovki [Hybrid approach for solving the problem of 3-dimensional packaging], Izvestiya YuFU.
Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2016, No. 6 (179), pp. 45-51.
5. Lutsan M.V., Nuzhnov E.V. Reshenie zadachi trekhmernoy upakovki s paletirovaniem konteynerov
[Solution of the problem of three-dimensional packaging with palletizing of containers], Izvestiya
YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2014, No. 7 (156), pp. 196-204.
6. Mladenović N., Hansen P. Variable neighborhood search, Computers & Operations Research, 1997,
Vol. 24, pp. 1097-1100.
7. Киркпатрик С. Гелат-младший К.Д. Викки М.П. Оптимизация с помощью имитации отжига //
Наука. – 1983. – 220. – С. 671-680.
8. Nicholas Metropolis, Arianna W. Rosenbluth, Mar - shall N. Rosenbluth, Augusta H. Teller, Edward
Teller. Equation of state calculations by fast computing machines, Journal of Chemical Physics, 1953,
Vol. 21, No. 6, pp. 1087-1092.
9. Kadowaki T., Nishimori H. Quantum annealing in the transverse Ising model, Phys. Rev. E, 1998,
Vol. 58, No. 5, pp. 5355-5363.
10. Resende M.G., Ribeiro C. Greedy Randomized Adaptive Search Procedures: Advances and Extensions,
Published in Handbook of Metaheuristics, 2018, Vol. 272, pp. 169-220.
11. Land A.H., and Doig A.G. An automatic method of solving discrete programming problems,
Econometrica, 1960, Vol. 28, pp. 497-520.
12. Fred Glover. Tabu Search – Part 1, ORSA Journal on Computing, 1989, Vol. 1, No. 3, pp. 190-206.
13. Suganya M., Sasipraba T. Stochastic Gradient Descent long short-term memory based secure encryption
algorithm for cloud data storage and retrieval in cloud computing environment, Journal of Cloud
Computing, 2023, Vol. 12, pp. 1-17.
14. Karpenko A.P. Sovremennye algoritmy poiskovoy optimizatsii. Algoritmy, vdokhnovlennye prirodoy:
ucheb. posobie [Modern algorithms for search engine optimization. Algorithms inspired by nature: a
tutorial]. 3rd. ed. Moscow: Izd-vo: MGTU im. N.E. Baumana. 2021, 446 p.
15. Gladkov L.A., Kravchenko Yu.A., Kureychik V.V., Rodzin S.I. Intellektual'nye sistemy: Modeli i
metody metaevristicheskoy optimizatsii: monografiya [Intelligent systems: Models and methods of
metaheuristic optimization: monograph]. Cheboksary: Sreda, 2024, 228 p.
16. Holland, John H. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Application
to Biology, Control, and Artificial Intelligence. USA: University of Michigan. 1975, 183 p.
17. Kureichik V.M., Malioukov S.P., Kureichik V.V., Malioukov A.S. Genetic algorithms for applied CAD
problems. Berlin, 2009.
18. Kureychik V.V. Rodzin S.I. Vychislitel'nye modeli evolyutsionnykh i roevykh bioevristik (obzor),
Informatsionnye tekhnologii, 2021, Vol. 27, No. 10, pp. 507-520.
19. Storn R., Price K. Differential evolution - a simple and efficient heuristic for global optimization over
continuous spaces, Journal of Global Optimization, 1997, Vol. 11, No. 4, pp. 341-359.
20. Hassanien E., Emary E. Swarm Intelligence: Principles Advances, and Applications. CRC Press:
2015, 228 p.
21. Kennedy J., Eberhart R.C. Particle swarm optimization, In Proceedings of IEEE International Conference
on Neural Networks, 1995, Vol. 4, pp. 1942-1948.
22. Dorigo M., Maniezzo V., Colorni A. The Ant System: Optimization by a colony of cooperating objects,
IEEE Trans. on Systems, Man, and Cybernetics, 1996, Part B, Vol. 26, No. 1, pp. 29-41.
23. Karaboga D. An idea based on honey bee swarm for numerical optimization. Erciyes University, Engineering
Faculty, Computer Engineering Department, 2005, 110 p.
24. Xin-She Yang. Firefly algorithms for multimodal optimization, In International symposium on stochastic
algorithms, 2009, pp. 169-178.
25. Mirjalili S. Dragonfly algorithm: a new meta-heuristic optimization technique for solving singleobjective,
discrete, and multi-objective problems, Neural Computing and Applications, 2016, Vol. 27,
pp. 1053-1073.
26. Hamed Shah-Hosseini. Intelligent water drops algorithm: a new optimization method for solving the
multiple knapsack problem, International Journal of Intelligent Computing and Cybernetics, 2008,
Vol. 1, No. 2, pp. 193-212.
27. Zhao W., Wang L., Zhang Z. Artificial ecosystem-based optimization: a novel nature-inspired metaheuristic
algorithm, In: Neural Computing and Applications, 2020, Vol. 32, No. 13, pp. 9383-9425.
28. Odili J.B., Kahar M.N.M. African Buffalo Optimization: A Swarm-Intelligence Technique, Procedia
Computer Science, 2015, Vol. 76, pp. 443-448.
29. Mehrabian A.R., Lucas C.A. Novel numerical optimization algorithm inspired from weed colonization,
Ecological Informatics, 2006, Vol. 1, No. 4, pp. 355-366.
30. Panteleev A.V., Metlitskaya D.V., Aleshina E.A. Metody global'noy optimizatsii. Metaevristicheskie
strategii i algoritmy [Methods of global optimization. Metaheuristic strategies and algorithms]. Moscow:
Vuzovskaya kniga, 2013, 244 p.
