Solving a Dynamic Scheduling Problem for a Manufacturing System with Reinforcement Learning

Author:

Heik David,Bahrpeyma Fouad,Reichelt Dirk

Publisher

Springer Nature Switzerland

Reference29 articles.

1. Bahrpeyma, F., Haghighi, H., Zakerolhosseini, A.: An adaptive rl based approach for dynamic resource provisioning in cloud virtualized data centers. Computing 97, 1209–1234 (2015)

2. Bahrpeyma, F., Reichelt, D.: A review of the applications of multi-agent reinforcement learning in smart factories. Front. Robot. AI 9 (2022)

3. Bahrpeyma, F., Zakerolhoseini, A., Haghighi, H.: Using ids fitted q to develop a real-time adaptive controller for dynamic resource provisioning in cloud’s virtualized environment. Appl. Soft Comput. 26, 285–298 (2015)

4. Carroll, D.C.: Heuristic sequencing of single and multiple component jobs. Ph.D. thesis, Massachusetts Institute of Technology (1965)

5. Chanda, A.K., Chawla, V., Angra, S.: A modified memetic particle swarm optimization algorithm for sustainable multi-objective scheduling of automatic guided vehicles in a flexible manufacturing system. Int. J. Comput. Aided Manuf. 4(1), 33–47 (2018)

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