Warianty tytułu
The application of a genetic algorithm for scheduling material flow in complex production systems
Języki publikacji
Abstrakty
Modern production systems are characterised by a high degree of complexity, resulting from the use of many different technological processes, the parallel production of complex products, the use of advanced numerically controlled (CNC) machine tools and complex transport systems. At the same time, it is necessary to take into account many variables and constraints such as the availability of machines, tools and workers, stock levels in the warehouse, forecasted product demand, material handling capacities and the sequence in which individual tasks must be performed. Effective planning of the flow of items in such systems is key to achieving high productivity, minimising costs and ensuring on-time delivery. Traditional planning methods often prove insufficient in the face of dynamic and unpredictable production conditions. In this context, genetic algorithms (AG) represent a promising tool for optimising production processes. This paper presents an example of the application of a genetic algorithm to optimise the production process in an exemplary robotic production system. As the main optimisation criterion, the total sum of delays to be reckoned with when accepting a defined set of orders for execution. In addition, the total execution time for the set of orders and the machine tool utilisation rates during the entire production process were analysed. In order to be able to apply the genetic algorithm, it was necessary to build a parametric simulation model and integrate this model with the developed genetic algorithm. The simulation model was used to determine the objective function in the optimisation process implemented by the genetic algorithm.(original abstract)
Rocznik
Tom
Numer
Strony
16-22
Opis fizyczny
Twórcy
autor
- West Pomeranian University of Technology in Szczecin, Poland
autor
- West Pomeranian University of Technology in Szczecin, Poland
Bibliografia
- Jardzioch A., Skobiej B., Zastosowanie algorytmu wsadowego do szeregowania zadań produkcyjnych, Podstawy Foundations of Computing and Decision Sciences, 36, 3-4, 207-217, 2011.
- Jardzioch A., Witkowska W., Zastosowanie logiki rozmytej do szeregowania zleceń produkcyjnych, Zarządzanie Przedsiębiorstwem, Enterprise Management, 25, 3-4, 25-36, 2022.
- Jardzioch A., Skobiej B., Job scheduling problem in a flow shop system with simulated hardening, Advances in Manufacturing, 13, 391-400, 2018
- Kinast A., Doerner K.F., Rinderle-Ma S., Biased random-key genetic algorithm for cobot assignment in an assembly/disassembly job shop scheduling problem, 22 Procedia Comput. Sci., Proceedings of the 2nd International Conference on Industry 4.0 and Smart Manufacturing (ISM 2020), 180, 328-337, 2021.
- Patalas-Maliszewska J., Kłos S., Dostatni E., Integrating the assessment of sustainability and an ERP system in small and medium manufacturing enterprise- A case study, [in:] Trojanowska J., Kujawińska A., Machado J., Pavlenko I. [Eds.], Advances in Manufacturing III. MANUFACTURING 2022. Lecture Notes in Mechanical Engineering, Springer, Cham, 2022, doi: 10.1007/978-3-030-99310-8 5.
- Ruiz-Rodr´ ıguez M.L., Kubler S., Robert J., Le Traon Y., Dynamic maintenance scheduling approach under uncertainty: Comparison between reinforcement learning, genetic algorithm simheuristic, dispatching rules, Expert Syst. Appl., 248, 123404, 2024.
- Wang H.-K., Chou C.-W., Wang C.-H., Ho L.-A., Sustainable scheduling of TFT-LCD cell production: A hybrid dispatching rule and two-phase genetic algorithm, Int. J. Prod. Econ., 278, 109412, 2024, doi: 10.1016/j.ijpe.2024.109412.
- Wang Y.-C., Chen T., Adapted techniques of explainable artificial intelligence for explaining genetic algorithms on the example of job scheduling, Expert Syst. Appl., 237, 121369, 2024, doi: 10.1016/j.eswa.2023.
- Wen X., Qian Y., Lian X., Li H., Wang H., Zhang Y., Improved genetic algorithm for integrated process planning and scheduling in distributed heterogeneous manufacturing environment, Eng. Appl. Artif. Intell., 133, 108569, 2024, doi: 10.1016/j.engappai.2024.108569.
- Wen X., Zhang X., Xing H., Ye G., Li H., Zhang Y., Wang H., An improved genetic algorithm based on reinforcement learning for aircraft assembly scheduling problem, Comput. Ind. Eng., 193, 110263, 2024, doi: 10.1016/j.cie.2024.110263.
- Xie J., Li X., Gao L., Gui L., A hybrid genetic tabu search algorithm for distributed flexible job shop scheduling problems, J. Manuf. Syst., 71, 82-94, 2023, doi: 10.1016/j.jmsy.2023.09.002.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
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