Testing algorithms for quick rescheduling flow shop problems with FlexSim based simulation and R engine

• Autorzy: Janke Piotr

Identyfikatory

DOI

10.29119/1641-3466.2023.168.12

• Języki publikacji: EN

Abstrakty

EN

Purpose: The aim of this paper is to present a combination of advanced algorithms for finding optimal solutions together with their tests for a permutation flow-shop problem with the possibilities offered by a simulation environment. Four time-constrained algorithms are tested and compared for a specific problem. Design/methodology/approach: Four time-constrained algorithms are tested and compared for a specific problem. The results of the work realisation of the algorithms are transferred to a simulation environment. The entire solution proposed in the work is composed as a parallel environment to the real implementation of the production process. Findings: The genetic algorithm generated the best solution in the same specified short time. By implementing the adopted approach, the correct cooperation of the FlexSim simulation environment with the R language engine was obtained. Research limitations/implications: The genetic algorithm generated the best solution in the same specified short time. By implementing the approach, a correct interaction between the FlexSim simulation environment and the R language engine was achieved. Practical implications: The solution proposed in this paper can be used as an environment to test solutions proposed in production. Simulation methods in the areas of logistics and production have for years attracted the interest of the scientific community and the wider industry. Combining the achievements of science in solving computationally complex problems with increasingly sophisticated algorithms, including artificial intelligence algorithms, with simulation methods that allow a detailed overview of the consequences of changes made seems promising. Originality/value: The original concept of cooperation between the R environment and the FlexSim simulation software for a specific problem was presented.

Słowa kluczowe

EN

flow-shop problem; genetic algorithm; simulation

PL

problem przepływowy; algorytm genetyczny; symulacja

• Wydawca: Wydawnictwo Politechniki Śląskiej
• Czasopismo: Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska
• Rocznik: 2023
• Tom: z. 168
• Strony: 163--175
• Opis fizyczny: Bibliogr. 13 poz.

Twórcy

autor

Janke Piotr

• Silesian University of Technology, Faculty of Organization and Management

• https://orcid.org/0000-0001-8065-9013

Bibliografia

• 1. Andrade, C.E., Silva, T., Pessoa, L.S. (2019). Minimizing flowtime in a flowshop scheduling problem with a biased random-key genetic algorithm. Expert Systems with Applications, 128, 67-80. https://doi.org/https://doi.org/10.1016/j.eswa.2019.03.007.
• 2. Carey, M.V. (2015). Package ‘combinat’.
• 3. Guido, R., Conforti, D. (2017). A hybrid genetic approach for solving an integrated multi- objective operating room planning and scheduling problem. Computers & Operations Research, 87, 270-282. https://doi.org/https://doi.org/10.1016/j.cor.2016.11.009.
• 4. Huynh, N.-T., Chien, C.-F. (2018). A hybrid multi-subpopulation genetic algorithm for textile batch dyeing scheduling and an empirical study. Computers & Industrial Engineering, 125, 615-627. https://doi.org/https://doi.org/10.1016/j.cie.2018.01.005.
• 5. Jankauskas, K., Papageorgiou, L.G., Farid, S.S. (2019). Fast genetic algorithm approaches to solving discrete-time mixed integer linear programming problems of capacity planning and scheduling of biopharmaceutical manufacture. Computers & Chemical Engineering, 121, 212-223. https://doi.org/https://doi.org/10.1016/j.compchemeng.2018.09.019.
• 6. Kaczmar, I., Bányai, T. (2022). Optimisation of flow shop scheduling problem: simulation system vs. evolutive solver. Advanced Logistic Systems - Theory and Practice, 16(1), 31-40. https://doi.org/10.32971/als.2022.003.
• 7. Knosala, R., Wal, T. (2001). Production scheduling problem using genetic algorithm. Journal of Materials Processing Technology, 109(1-2), 90-95. https://doi.org/10.1016/S0924-0136(00)00780-9.
• 8. Ławrynowicz, A. (2011). Genetic Algorithms for Solving Scheduling Problems in Manufacturing Systems. Foundations of Management, 3(2), 7-26. https://doi.org/10.2478/v10238-012-0039-2.
• 9. Pang, X., Xue, H., Tseng, M., Lim, M.K., Liu, K. (2020). Applied Sciences Improved Fireworks Algorithm for Permutation.
• 10. Pawlak, M. (1999). Algorytmy ewolucyjne jako narzędzie harmonogramowania produkcji. Warszawa: PWN.
• 11. Ramesh, C., Kamalakannan, R., Karthik, R., Pavin, C. (2020). A lot streaming based flow shop scheduling problem using simulated annealing algorithm. Materials Today: Proceedings. https://doi.org/https://doi.org/10.1016/j.matpr.2020.05.108.
• 12. Rolf, B., Reggelin, T., Nahhas, A., Lang, S., Müller, M. (2020). Assigning dispatching rules using a genetic algorithm to solve a hybrid flow shop scheduling problem. Procedia Manufacturing, 42, 442-449. Elsevier B.V. https://doi.org/10.1016/j.promfg.2020.02.051.
• 13. Rossit, D.A., Tohmé, F., Frutos, M. (2018). The Non-Permutation Flow-Shop scheduling problem: A literature review. Omega, 77, 143-153. https://doi.org/10.1016/J.OMEGA.2017.05.010.