Artificial intelligence methods for short-term planning in petroleum products realization

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In this article, a critical analytical review of the application of artificial intelligence methods in the field of scheduling theory is presented, exemplified by the constraints of the short-term planning problem in the process of petroleum products realization via road transport. The objective of the research was to systematize and evaluate existing approaches to solving planning tasks while considering specific temporal constraints inherent to the petroleum products realization process. During the study, both exact and approximate methods for solving scheduling theory problems were analyzed, including heuristic algorithms and approaches based on artificial neural networks. It was established that existing methods have significant limitations when addressing semi-online planning tasks. The research findings demonstrate the necessity for developing a new method capable of promptly restructuring schedules in response to unpredictable changes that arise during the petroleum products realization process. The results of the study highlight the promising potential for advancing artificial intelligence methods to address short-term planning challenges.

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作者简介

Yuriy Ignatyev

Bauman Moscow State Technical University

编辑信件的主要联系方式.
Email: Yuriy-Ig@yandex.ru

postgraduate student

俄罗斯联邦, Moscow

Gennady Afanasyev

Bauman Moscow State Technical University

Email: gaipcs@bmstu.ru

Cand. Sci. (Eng.), Associate Professor; associate professor

俄罗斯联邦, Moscow

参考

  1. Kantorovich L. Mathematical methods of production planning and organization. Leningrad: Leningrad State University Press, 1939.
  2. Krivosheev O.V. Resource allocation technology for production systems under data uncertainty in high-tech industries. Dis. ... of Cand. Sci. (Eng.). Sarov, 2022.
  3. Krotov K.V. Mathematical models and methods for multilevel scheduling optimization of multistage processes with adaptation. Dis. ... of Dr. Sci. (Eng.). Sevastopol, 2022.
  4. Lazarev A.A., Gafarov E.R. Scheduling theory: Problems and algorithms. Moscow: Faculty of Physics, Moscow State University, 2011. 222 p.
  5. Lazarev A.A. et al. Scheduling theory: Railway planning problems. Moscow: Institute of Control Sciences, RAS, 2021. 92 p.
  6. Tanaev V.S., Shkurba V.V. Introduction to scheduling theory. by Yu.D. Yudin (ed.). Moscow: Nauka, 1975. 256 p.
  7. Agnetis A. et al. Fifty years of research in scheduling – theory and applications. Eur. J. Oper. Res. 2025. doi: 10.1016/j.ejor.2025.01.034.
  8. Bellman R. Mathematical aspects of scheduling theory. Journal of the Society for Industrial and Applied Mathematics. 1956. Vol. 4. No. 3. doi: 10.1137/0104010.
  9. Blackstone J.H., Phillips D.T., Hogg G.L. A state-of-the-art survey of dispatching rules for manufacturing job shop operations. Int. J. Prod Res. 1982. Vol. 20. No. 1. doi: 10.1080/00207548208947745.
  10. Brucker P. Scheduling algorithms. Fifth ed. Berlin: Springer-Verlag, 2007. 365 с.
  11. Cruz-Chávez M.A., Martínez-Rangel M.G., Cruz-Rosales M.H. Accelerated simulated annealing algorithm applied to the flexible job shop scheduling problem. International Transactions in Operational Research. 2017. Vol. 24. No. 5. doi: 10.1111/itor.12195.
  12. Dürr Ch. The Scheduling Zoo. URL: https://github.com/xtof-durr/schedulingzoo/wiki/The-Scheduling-Zoo-project (data of accesses: 05.06.2024).
  13. Gantt N.L. A Graphical daily balance in manufacture. Journal of Fluids Engineering, Transactions of the ASME. 1903.
  14. Garey M.R., Johnson D.S., Sethi R. The complexity of flow shop and job shop scheduling. Mathematics of Operations Research. 1976. Vol. 1. No. 2. Pp. 117–129. doi: 10.1287/moor.1.2.117.
  15. Graham R.L. et al. Optimization and approximation in deterministic sequencing and scheduling: A survey. Annals of Discrete Mathematics. 1979. Pp. 287–326.
  16. Haddad N., Myshenkov K.S., Afanasiev G.I. Introducing text analysis algorithms in decision support systems for automated evaluation of the doctor prescriptions. In: 6th International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE). IEEE, 2024. Pp. 1–5.
  17. Hasan S.M.K. et al. Memetic algorithms for solving job-shop scheduling problems. Memet. Comput. 2009. Vol. 1. No. 1. Pp. 69–83. doi: 10.1007/s12293-008-0004-5.
  18. Hopfield J.J., Tank D.W. “Neural” computation of decisions in optimization problems. Biol. Cybern. 1985. Vol. 52. No. 3. Pp. 141–152. doi: 10.1007/BF00339943.
  19. Hu Y., Duan Q. Solving the TSP by the AALHNN algorithm. Mathematical Biosciences and Engineering. 2022. Vol. 19. No. 4. Pp. 3427–3448. doi: 10.3934/mbe.2022158.
  20. Ignatyev Y.V., Afanasyev G.I. Neural network architecture for scheduling tank trucks loading at petroleum products storages. In: 7th International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE). Moscow: IEEE, 2025.
  21. Ivanyuk V., Shuvalov K. Neural network-based methods for forecasting financial time series. In: 14th International Conference Management of Large-scale System Development (MLSD). IEEE, 2021. Pp. 1–4.
  22. James R.J. Scheduling a production line to minimize maximum tardiness. Los Angeles: Office of Technical Services, 1955.
  23. Johnson S.M. Optimal two‐ and three‐stage production schedules with setup times included. Naval Research Logistics Quarterly. 1954. Vol. 1. No. 1. doi: 10.1002/nav.3800010110.
  24. Jun S., Lee S., Chun H. Learning dispatching rules using random forest in flexible job shop scheduling problems. Int. J. Prod. Res. 2019. Vol. 57. No. 10. Pp. 3290–3310. doi: 10.1080/00207543.2019.1581954.
  25. Khobotov E.N., Ermolova M.A. Formation of work plans and schedules at enterprises with conveyor assembly. IFIP Advances in Information and Communication Technology. 2021. Pp. 572–579.
  26. Kirkpatrick S., Gelatt C.D., Vecchi M.P. Optimization by simulated annealing. Science (1979). 1983. Vol. 220. No. 4598. Pp. 671–680. doi: 10.1126/science.220.4598.671.
  27. Lenstra J.K., Rinnooy Kan A.H.G., Brucker P. Complexity of machine scheduling problems. Annals of Discrete Mathematics. 1977. Vol. 1. No. C. doi: 10.1016/S0167-5060(08)70743-X.
  28. Li F. et al. A transformer-based deep reinforcement learning approach for dynamic parallel machine scheduling problem with family setups. J. Intell Manuf. 2024. doi: 10.1007/s10845-024-02470-8.
  29. Li X. et al. Integrated optimization approach of hybrid flow-shop scheduling based on process set. IEEE Access. 2020. Vol. 8. Pp. 223782–223796. doi: 10.1109/ACCESS.2020.3044606.
  30. Michael L.P. Scheduling theory, algorithms, and systems. Sixth ed. New York: Springer, 2022.
  31. Noorul Haq A. et al. A hybrid neural network-genetic algorithm approach for permutation flow shop scheduling. Int. J. Prod. Res. 2010. Vol. 48. No. 14. Pp. 4217–4231. doi: 10.1080/00207540802404364.
  32. Parthasarathy S., Rajendran C. An experimental evaluation of heuristics for scheduling in a real-life flowshop with sequence-dependent setup times of jobs. Int. J. Prod. Econ. 1997. Vol. 49. No. 3. Pp. 255–263. doi: 10.1016/S0925-5273(97)00017-0.
  33. Richard W. et al. Theory of scheduling, 1967.
  34. Saidi-Mehrabad M., Fattahi P. Flexible job shop scheduling with taboo search algorithms. International Journal of Advanced Manufacturing Technology. 2007. Vol. 32. No. 5–6. doi: 10.1007/s00170-005-0375-4.
  35. Saprykin Y., Ryazntsev V., Smirnov A. Application of neural networks to the analysis of time series data in the recognition of driver fatigue. In: International Conference on Information Technology and Nanotechnology (ITNT). IEEE, 2021. Pp. 1–5.
  36. Smith W.E. Various optimizers for single‐stage production. Naval Research Logistics Quarterly. 1956. Vol. 3. No. 1–2. doi: 10.1002/nav.3800030106.
  37. Tassel P., Gebser M., Schekotihin K. A reinforcement learning environment for job-shop Scheduling. International Journal of Computer Information Systems and Industrial Management Applications. 2021. Vol. 12.
  38. Yazdani M. et al. A simulated annealing algorithm for flexible job-shop scheduling problem. Journal of Applied Sciences. 2009. Vol. 9. No. 4. doi: 10.3923/jas.2009.662.670.

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