Abstract
Production scheduling of advanced manufacturing systems has attracted significant attention by both researchers and industrial practitioners in recent years. Due to the complexity of these systems, the generation of production schedules requires an intelligent technique. Many artificial intelligence techniques such as fuzzy logic (FL), genetic algorithms (GA) and neural networks (NN) have been successfully applied to the scheduling of advanced manufacturing systems.
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Abd, K., Abhary, K., & Marian, R. (2011). Scheduling and performance evaluation of robotic flexible assembly cells under different dispatching rules. Advances in Mechanical Engineering, 1(1), 31–40.
Abd, K., Abhary, K., & Marian, R. (2012a). Efficient scheduling rule for robotic flexible assembly cells based on fuzzy approach. In Proceedings of the 45th CIRP Conference on Manufacturing Systems (vol. 3, no. 3, pp. 483–488), Athens, Greece.
Abd, K., Abhary, K., & Marian, R. (2012b). Intelligent modeling of scheduling robotic flexible assembly cells using fuzzy logic. In 12th WSEAS International Conference on Robotics, Control and Manufacturing Technology (pp. 202–207), Rovaniemi, Finland.
Abd, K., Abhary, K., & Marian, R. (2013a). Development of a fuzzy-simulation model of scheduling robotic flexible assembly cells. Journal of Computer Science, 9(12), 1761–1768.
Abd, K., Abhary, K., & Marian, R. (2013b). Intelligent model of scheduling RFACs—part I: Methodology and strategy. In DAAAM international scientific book (pp. 719–736). Vienna: DAAAM International Publishing.
Abd, K., Abhary, K., & Marian, R. (2013c). A methodology for scheduling robotic flexible assembly cells using fuzzy logic and simulation (pp. 449–454). London, UK: Proceedings of the World Congress on Engineering.
Baptiste, P., & Schieber, B. (2003). ‘A note on scheduling tall/small multiprocessor tasks with unit processing time to minimize maximum tardiness. Journal of Scheduling, 6(4), 395–404.
Berrichi, A., & Yalaoui, F. (2013). Efficient bi-objective ant colony approach to minimize total tardiness and system unavailability for a parallel machine scheduling problem. The International Journal of Advanced manufacturing Technology, 68(9), 2295–2310.
Bilkay, O., Anlagan, O., & Kilic, S. E. (2004). Job shop scheduling using fuzzy logic. International Journal of Advanced Manufacturing Technology, 23(7), 606–619.
Buil, R., Piera, M. A., & Luh, P. B. (2010). Improvement of lagrangian relaxation convergence for production scheduling. IEEE Transactions on Automation Science and Engineering, 9(1), 137–147.
CACI. (2006). User’s manual: Simprocess. La Jolla, CA: CACI Products Company.
Canbolat, Y. B., & Gundogar, E. (2004). Fuzzy priority rule for job shop scheduling. Journal of Intelligent Manufacturing, 15(4), 527–533.
Chan, T. S. F., & Chan, K. H. (2004). A comprehensive survey and future trend of simulation study on FMS scheduling. Journal of Intelligent Manufacturing, 15(1), 87–102.
Danping, L., & Lee, C. K. M. (2010). A review of the research methodology for the re-entrant scheduling problem. International Journal of Production Research, 49(8), 2221–2242.
Desal, N. K. (1997). Scheduling algorithm for flexible manufacturing cells. Master of Science, University of Manitoba.
Domingos, J. C., & Politano, P. R. (2003). On-line scheduling for flexible manufacturing systems based on fuzzy logic. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 5, 4928–4933.
Jayamohan, M. S., & Rajendran, C. (2000). New dispatching rules for shop scheduling: A step forward. International Journal of Production Research, 38(3), 563–586.
Kumar, R. R., Singh, A. K., & Tiwari, M. K. (2004). A fuzzy based algorithm to solve the machine-loading problems of a FMS and its neuro fuzzy Petri net model. International Journal of Advanced Manufacturing Technology, 23(5), 318–341.
Mahdavi, I., Fekri Moghaddam Azar, A. H., & Bagherpour, M. (2009). Applying fuzzy rule based to flexible routing problem in a flexible manufacturing system. In Proceedings of the IEEE International Conference on Industrial Engineering and Engineering Management (pp. 2358–2364).
Marian, R. M., Kargas, A., Luong, L. H. S., & Abhary, K. (2003). A framework to planning robotic flexible assembly cells. In 32nd International Conference on Computers and Industrial Engineering (pp. 607–615), Limerick, Ireland.
Mendel, J. M. (1995). Fuzzy logic systems for engineering: A tutorial. IEEE, 83, 345–377.
Ramasesh, R. (1990). Dynamic job shop scheduling: A survey of simulation research. OMEGA. International Journal of Management Science, 18(1), 43–57.
Restrepo, I. M., & Balakrishnan, S. (2008). Fuzzy-based methodology for multi-objective scheduling in a robot-centered flexible manufacturing cell. Journal of Intelligent Manufacturing, 19(4), 421–432.
Sridhar, S., Prabaharan, T., & Saravanan, M. (2010). Optimisation of sequencing and scheduling in hybrid flow shop environment using heuristic approach. International Journal of Logistics Economics and Globalisation, 2(4), 331–351.
Srinoi, P., Minyong, P., Shayan, E., & Ghotb, F. (2008). Routing and sequencing determination in flexible manufacturing system using a fuzzy logic approach. Asian International Journal of Science and Technology in Production and Manufacturing, 1(2), 127–138.
Srinoi, P., Shayan, E., & Ghotb, F. (2006). A fuzzy logic modelling of dynamic scheduling in FMS. International Journal of Production Research, 44(11), 2183–2203.
Subramaniam, V., Ramesh, T., Lee, G. K., Wong, Y. S., & Hong, G. S. (2000). Job shop scheduling with dynamic fuzzy selection of dispatching rules. International Journal of Advanced Manufacturing Technology, 16(10), 759–764.
Swegles, S. (1997). Business process modeling with SIMPROCESS. In Winter Simulation Conference (pp. 606–610). Piscataway, NJ.
Tavakkoli-Moghaddam, R., Moslehi, G., Vasei, M., & Azaron, A. (2005). Optimal scheduling for a single machine to minimize the sum of maximum earliness and tardiness considering idle insert. Applied Mathematics and Computation, 167(2), 1430–1450.
Vidyarthi, N. K., & Tiwari, M. K. (2001). Machine loading problem of FMS: A fuzzy-based heuristic approach. International Journal of Production Research, 39(5), 953–957.
Xing, L. N., Chen, Y. W., Wang, P., Zhao, Q. S., & Xiong, J. (2010). A knowledge-based ant colony optimization for flexible job shop scheduling problems. Applied Soft Computing, 10(3), 888–896.
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Abd, K.K. (2016). Development of an Intelligent Methodology for Scheduling RFAC. In: Intelligent Scheduling of Robotic Flexible Assembly Cells. Springer Theses. Springer, Cham. https://doi.org/10.1007/978-3-319-26296-3_3
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