Open Access
| Issue |
EPJ Web Conf.
Volume 336, 2025
International Conference on Sustainable Development in Advanced Materials, Manufacturing, and Industry 4.0 (INSDAM’25)
|
|
|---|---|---|
| Article Number | 03004 | |
| Number of page(s) | 11 | |
| Section | Industry 4.0 | |
| DOI | https://doi.org/10.1051/epjconf/202533603004 | |
| Published online | 26 September 2025 | |
- S. A. Irani, H. Y. Koo, and S. Raman, “Feature-based operation sequence generation in CAPP,” Int. J. Prod. Res., vol. 33, no. 1, pp. 17–39, 1995, doi: 10.1080/00207549508930135. [Google Scholar]
- A. Mokhtar and X. Xu, “Machining precedence of 21/2D interacting features in a feature-based data model,” J. Intell. Manuf., vol. 22, no. 2, pp. 145–161, 2011, doi: 10.1007/s10845-009-0268-8. [Google Scholar]
- P. Prabhu, S. Elhence, H. Wang, and R. Wysk, “An operations network generator for computer aided process planning,” J. Manuf. Syst., vol. 9, no. 4, pp. 283–291, 1990, doi: 10.1016/0278-6125(90)90036-H. [Google Scholar]
- R. Weill, G. Spur, and W. Eversheim, “Survey of Computer-Aided Process Planning Systems,” CIRP Ann. - Manuf. Technol., vol. 31, no. 2, pp. 539–551, 1982, doi: 10.1016/S0007-8506(07)60176-0. [Google Scholar]
- Y. M. Chen and C.-T. Lin, “Optimizing the Operation Sequence of a Multihead Surface Mounting Machine Using a Discrete Particle Swarm Optimization Algorithm,” J. Artif. Evol. Appl., vol. 2008, p. 315950, 2008, doi: 10.1155/2008/315950. [Google Scholar]
- S. Singh and S. Deb, “An intelligent methodology for optimising machining operation sequence by ant system algorithm,” Int. J. Ind. Syst. Eng., vol. 16, no. 4, pp. 451–471, 2014, doi: 10.1504/IJISE.2014.060654. [Google Scholar]
- A. G. Krishna and K. Mallikarjuna Rao, “Optimisation of operations sequence in CAPP using an ant colony algorithm,” Int. J. Adv. Manuf. Technol., vol. 29, no. 1–2, pp. 159–164, 2006, doi: 10.1007/s00170-004-2491-y. [Google Scholar]
- K. Lian, C. Zhang, X. Shao, and Y. Zeng, “A multi-dimensional tabu search algorithm for the optimization of process planning,” Sci. China Technol. Sci., vol. 54, no. 12, pp. 3211–3219, 2011, doi: 10.1007/s11431-011-4594-7. [Google Scholar]
- L. X. Phung, D. Van Tran, S. V. Hoang, and S. H. Truong, “Effective method of operation sequence optimization in CAPP based on modified clustering algorithm,” J. Adv. Mech. Des. Syst. Manuf., vol. 11, no. 1, pp. 1–12, 2017, doi: 10.1299/jamdsm.2017jamdsm0001. [Google Scholar]
- S. V. B. Reddy, M. S. Shunmugam, and T. T. Narendran, “Operation sequencing in CAPP using genetic algorithms,” Int. J. Prod. Res., vol. 37, no. 5, pp. 1063–1074, 1999, doi: 10.1080/002075499191409. [Google Scholar]
- T. Dereli and I. H. Filiz, “Optimisation of process planning functions by genetic algorithms,” Comput. Ind. Eng., vol. 36, no. 2, pp. 281–308, 1999, doi: 10.1016/S0360-8352(99)00133-3. [Google Scholar]
- C. Moon, J. Kim, G. Choi, and Y. Seo, “An efficient genetic algorithm for the traveling salesman problem with precedence constraints,” Eur. J. Oper. Res., vol. 140, no. 3, pp. 606–617, 2002, doi: 10.1016/S0377-2217(01)00227-2. [Google Scholar]
- F. Zhang, Y. F. Zhang, and A. Y. C. Nee, “Using genetic algorithms in process planning for job shop machining,” IEEE Trans. Evol. Comput., vol. 1, no. 4, pp. 278–289, 1997, doi: 10.1109/4235.687888. [Google Scholar]
- B. Toaza and D. Esztergár-Kiss, “A review of metaheuristic algorithms for solving TSP-based scheduling optimization problems [Formula presented],” Appl. Soft Comput., vol. 148, no. October, 2023, doi: 10.1016/j.asoc.2023.110908. [Google Scholar]
- F. Deng, P. Zeng, G. Li, Y. Wang, and B. Huang, “Study on three-dimensional permeability model of gravel sand control layer based on simulated annealing algorithm,” Int. J. Hydrogen Energy, vol. 91, pp. 106–117, 2024, doi: https://doi.org/10.1016/j.ijhydene.2024.10.112. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.