Development of information technology for planning order fulfillment at a food enterprise

Authors

DOI:

https://doi.org/10.15587/1729-4061.2020.195455

Keywords:

mathematical model, scheduling order execution, combined algorithm

Abstract

An information technology has been proposed that aims to resolve the task of planning the fulfillment of orders for manufacturing products at food enterprises under conditions of uncertainty and risk. The information technology is based on combining the ant colony, gray wolves, and genetic algorithms, as well as the constructed mathematical model of the operative execution of orders. The advantages of algorithm combination include the formation of alternative variants of plans and the avoidance of local optima. The proposed mathematical model includes the partial criteria, constraints, as well as an evaluation function, for determining the effectiveness of the compiled plan of order execution. The application of a petal diagram and an additive convolution of partial criteria has been suggested to illustrate the clarity of a variant of order fulfillment. The mathematical model makes it possible for a DM to define any set of partial criteria to take into consideration the patterns of order execution.

The information technology ensures rapid reconfiguration of the current plan of order execution in the event of emergencies or the need to urgently fulfill a certain order

Author Biographies

Serhii Hrybkov, National University of Food Technologies Volodymyrska str., 68, Kyiv, Ukraine, 01601

PhD, Associate Professor

Department of Information Systems

Olena Kharkianen, National University of Food Technologies Volodymyrska str., 68, Kyiv, Ukraine, 01601

PhD, Associate Professor

Department of Information Systems

Volodymyr Ovcharuk, National University of Food Technologies Volodymyrska str., 68, Kyiv, Ukraine, 01601

PhD, Associate Professor

Department of Informatic

Iryna Ovcharuk, State University of Infrastructure and Technologies Ivana Ohienka str., 19, Kyiv, Ukraine, 03049

PhD, Associate Professor

Department of Information Technologies

References

  1. Hrybkov, S., Oliinyk, H., Litvinov, V. (2018). Web­oriented decision support system for planning agreements execution. Eastern-European Journal of Enterprise Technologies, 3 (2 (93)), 13–24. doi: https://doi.org/10.15587/1729-4061.2018.132604
  2. Kharkianen, O., Myakshylo, O., Hrybkov, S., Kostikov, M. (2018). Development of information technology for supporting the process of adjustment of the food enterprise assortment. Eastern-European Journal of Enterprise Technologies, 1 (3 (91)), 77–87. doi: https://doi.org/10.15587/1729-4061.2018.123383
  3. Santosh, K. S., Vinod, K. G. (2015). Genetic Algorithms: Basic Concepts and Real World Applications. International Journal of Electrical, Electronics and Computer Systems (IJEECS), 3 (12), 116–123.
  4. Senthilkumar, K. M., Selladurai, V., Raja, K., Thirunavukkarasu, V. (2011). A Hybrid Algorithm Based on PSO and ACO Approach for Solving Combinatorial Fuzzy Unrelated Parallel Machine Scheduling Problem. European Journal of Scientific Research, 64 (2), 293–313.
  5. Lin, Y.-K. (2017). Scheduling efficiency on correlated parallel machine scheduling problems. Operational Research, 18 (3), 603–624. doi: https://doi.org/10.1007/s12351-017-0355-0
  6. Rodriguez, F. J., Lozano, M., García-Martínez, C., González-Barrera, J. D. (2013). An artificial bee colony algorithm for the maximally diverse grouping problem. Information Sciences, 230, 183–196. doi: https://doi.org/10.1016/j.ins.2012.12.020
  7. Sivaraj, R., Ravichandran, T., Devi Priya, R. (2012). Boosting Performance of genetic algorithm through Selective initialization. European Journal of Scientific Research, 68 (1), 93–100.
  8. Arendateleva, S. I. (2010). Matematicheskoe modelirovanie proizvodstvennogo planirovaniya na malom predpriyatii. Vestnik Tverskogo gosudarstvennogo universiteta. Seriya: Prikladnaya matematika, 2 (17), 97–109.
  9. Boyko, R., Hrybkov, S. (2019). Network structures for managing complex organizational-technical (technological) systems. Food Industry, 25, 116–123. Available at: http://nbuv.gov.ua/UJRN/Khp_2019_25_17
  10. Sobchak, A., Lutai, L., Fedorenko, M. (2019). Development of information technology elements for decision-making support aimed at re-structuring production at virtual instrument-making enterprises. Eastern-European Journal of Enterprise Technologies, 5 (4 (101)), 53–62. doi: https://doi.org/10.15587/1729-4061.2019.182039
  11. Georgiadis, G. P., Elekidis, A. P., Georgiadis, M. C. (2019). Optimization-Based Scheduling for the Process Industries: From Theory to Real-Life Industrial Applications. Processes, 7 (7), 438. doi: https://doi.org/10.3390/pr7070438
  12. Barker, K., Wilson, K. J. (2012). Decision Trees with Single and Multiple Interval-Valued Objectives. Decision Analysis, 9 (4), 348–358. doi: https://doi.org/10.1287/deca.1120.0253
  13. Kamiński, B., Jakubczyk, M., Szufel, P. (2017). A framework for sensitivity analysis of decision trees. Central European Journal of Operations Research, 26 (1), 135–159. doi: https://doi.org/10.1007/s10100-017-0479-6
  14. Oliynyk, H. V., Hrybkov, S. V. (2017). Modyfikovanyi ACO alhorytm pobudovy kalendarnoho planu vykonannia dohovoriv. Matematychne ta kompiuterne modeliuvannia. Seriya: Tekhnichni nauky, 15, 156–162.
  15. Wu, H.-S., Zhang, F.-M. (2014). Wolf Pack Algorithm for Unconstrained Global Optimization. Mathematical Problems in Engineering, 2014, 1–17. doi: https://doi.org/10.1155/2014/465082
  16. Zhang, Y., Balochian, S., Agarwal, P., Bhatnagar, V., Housheya, O. J. (2014). Artificial Intelligence and Its Applications. Mathematical Problems in Engineering, 2014, 1–10. doi: https://doi.org/10.1155/2014/840491
  17. Mirjalili, S., Mirjalili, S. M., Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46–61. doi: https://doi.org/10.1016/j.advengsoft.2013.12.007
  18. Madadi, A., Motlagh, M. (2014). Optimal Control of DC motor using Grey Wolf Optimizer Algorithm. Technical Journal of Engineering and Applied Science, 4 (4), 373–379.
  19. Yannibelli, V., Amandi, A. (2012). A deterministic crowding evolutionary algorithm to form learning teams in a collaborative learning context. Expert Systems with Applications, 39 (10), 8584–8592. doi: https://doi.org/10.1016/j.eswa.2012.01.195
  20. Jie Yao, Kharma, N., Grogono, P. (2010). Bi-Objective Multipopulation Genetic Algorithm for Multimodal Function Optimization. IEEE Transactions on Evolutionary Computation, 14 (1), 80–102. doi: https://doi.org/10.1109/tevc.2009.2017517

Published

2020-02-29

How to Cite

Hrybkov, S., Kharkianen, O., Ovcharuk, V., & Ovcharuk, I. (2020). Development of information technology for planning order fulfillment at a food enterprise. Eastern-European Journal of Enterprise Technologies, 1(3 (103), 62–73. https://doi.org/10.15587/1729-4061.2020.195455

Issue

Section

Control processes