Scheduling of the Manufacturing Cell Work with the Use of a Genetic Algorithm on the Example of a Flexible Production System

• Autorzy: Marcin Matuszny
• Języki publikacji: EN

Abstrakty

EN

The article is to present the application of genetic algorithm in production scheduling in a production company. In the research work the assumptions of the methodology were described and the operation of the proposed genetic algorithm was presented in details. Genetic algorithms are useful in complex large scale combinatorial optimisation tasks and in the engineering tasks with numerous limitations in the production engineering. Moreover, they are more reliable than the existing direct search algorithms. The research is focused on the effectivity improvement and on the methodology of scheduling of a manufacturing cell work. The genetic algorithm used in the work appeared to be robust and fast in finding accurate solutions. It was shown by experiment that using this method enables obtaining schedules suitable for a model. It ives a group of solutions that are at least as good as those created by the heuristic rules.(original abstract)

Słowa kluczowe

PL

Planowanie produkcji; Kontrola produkcji; Inżynieria produkcji; Proces produkcji; Algorytmy genetyczne

EN

Production planning; Production control; Production engineering; Production process; Genetic algorithms

• Czasopismo: Management and Production Engineering Review
• Rocznik: 2022
• Tom: 13
• Numer: nr 4
• Strony: 98-106

Twórcy

autor

Marcin Matuszny

• University of Bielsko-Biala, Bielsko-Biała, Poland

Bibliografia

• Ancveire I. and Poďaka I. (2019), Aplication of Genetic Algorithms for Decision-Making in Project Management: A Literature Review, Information Technology and Management Science, No. 22, pp. 22-31.
• Antoniol G., Di Penta M., and Harman M. (2004), Search-Based Techniques for Optimizing Software Project Resource Allocation, Genetic and Evolutionary Computation Conference, pp. 1425-1426.
• Binchao C. and Matis T.I. (2013), A flexible dispatching rule for minimizing tardiness in job shop scheduling, International Journal of Production Economics, No. 121, Vol. 1, pp. 360-365.
• Chen Y., Elliot M., and Sakshaug J. (2016), A genetic algorithm approach to synthetic data production, in: PrAISe 2016 Proceedings of the 1st International Workshop on AI for Privacy and Security, Hague, Netherlands.
• Chen Y., Elliot M., and Smith D. (2018), The Application of Genetic Algorithms to Data Synthesis: A Comparison of Three Crossover Methods, International Conference on Privacy in Statistical Databases, pp. 160171.
• Cheref A., Artigues C., and Billaut J.C. (2016), A new robust approach for a production scheduling and delivery routing problem, IFAC-PapersOnLine, 4 pp. 886891.
• Himmiche S., Marange P., Aubry A., and Petin J.F. (2018), Robust production scheduling under machine failures - A DES based evaluation approach, IFAC- PapersOnLine, No. 7, Vol. 51, pp. 271-276.
• Holland J.H. (1992), Adaptation in Natural and Artifical Systems: An Introductory Analysis with Applications to Biology, Control, and Artifical Intelligence, 4th printing 1995, Massachusetts Institute of Technology.
• Jardzioch A. and Skobiej B. (2013), Analysis of Variable Changeover Times Impact on the Revenue in Manufacturing Process, Academic Journal of Manufacturing Engineering, No. 11, Vol. 4, pp. 114-117.
• Jardzioch A. and Bulwan K. (2013), The Prioritisation of Production Orders Under the Bee Colony Algorithm, Advances in Manufacturing Science and Technology, No. 37, Vol. 4, pp. 49-59.
• Jin T., Cai Z., Li B., Zheng C., Jiang G., and Cheng J. (2020), Improving resource utilization by timely fine-grained scheduling, EuroSys '20: Proceedings of the Fifteenth European Conference on Computer Systems, pp. 1-16.
• Kwaśnicka H. (1999), Evolutionary computing in artificial intelligence, Publishing House of the Wrocław University of Technology.
• Lee H. and Kim S. (2001), Integration of process planning and scheduling using simulation based genetic algorithms, The International Journal of Advanced Manufacturing Technology, No. 18, pp. 586-590.
• Li H. and Dong X. (2018), Multi-mode resource levelling in projects with mode dependent generalized precedence relations, Expert Systems with Applications, No. 97, pp. 193-204.
• Michalewicz Z. (1999), Genetic algorithms + Data structure = Evolutionary programs, Scientific and Technical Publishing House.
• Paprocka I. and Skołud B. (2013), Robust Scheduling, a Production Scheduling Model of Failures, Applied Mechanics and Materials, No. 307, pp. 443-446.
• Rutkowska D., Piliński M., and Rutkowski L. (1999), Neural networks, genetic algorithms and fuzzy systems, Polish Scientific Publishers PWN. Rutczyńska-Wdowiak K. and Stefański T. (2010), Comparative analysis of classical optimization methods and genetic algorithm on the example of filter design, Measurements. Automation. Control, No. 65, Vol. 6, pp. 624-627.
• Samath S., Udalagama D., Kurukulasooriya H., Premarathne D., and Thelijjagoda S. (2017), Collabcrew- An intelligent tool for dynamic task allocation within a software development team , International Conference on Software, Knowledge Information, Industrial Management and Applications, Malabe, pp. 1-9.
• Savsani P., Banthia G., Gupta J., and Vyas R. (2016), Optimal Aggregate Production Planning by Using Genetic Algorithm, Proceedings of the 2016 International Conference on Industrial Engineering and Operations Management, Kuala Lumpur, Malaysia, pp. 863-874.
• Serrano-Ruiz J.C., Mula J., and Poler R. (2021), Smart manufacturing scheduling: A literature review, Journal of Manufacturing Systems, Vol. 61, pp. 265-287.
• Sobaszek Ł., Gola A., and Kozłowski E. (2020), Predictive Scheduling with Markov Chains and ARIMA Models, Applied Sciences, No. 17, Vol. 10.
• Stylianou C. and Andreou A.S. (2012), A multiobjective genetic algorithm for software development team staffing based on personality types, IFIP Advances in Information and Communication Technology, No. 381, Vol. 1, pp. 37-47.
• Stylianou C. and Andreou A.S. (2011), Intelligent software project scheduling and team staffing with genetic algorithms, IFIP Advances in Information and Communication Technology, No. 364, Vol. 2, pp. 169178.
• Wang X., Xie X., and Cheng T.C.E. (2013), A modified artificial bee colony algorithm for order acceptance in two-machine flow shops, International Journal Production Economics, No. 141, Vol. 1, pp. 14-23.
• Waschneck B., Reichstaller A., Belzner L., Altenmul- ler T., Bauernhansl T., Knapp A., and Kyek A. (2018), Optimization of global production scheduling with deep reinforcement learning, 51st CIRP Conference on Manufacturing Systems, Procedia CIRP 72, pp. 1264-1269.
• Wang C., Yang B., and Wang H.Q. (2020), Multiobjective master production schedule for balanced production of manufactures, International Journal of Simulation Modelling, No. 4, Vol. 19, pp. 678-688.

Identyfikatory

DOI

10.24425/mper.2022.142390