REFERENCES
1. Dang S, Tang W. Real-time data scheduling of flexible job in papermaking workshop based on deep learning and improved fuzzy algorithm. Mob Inf Syst 2021;2021:1-12.
2. Rager M, Gahm C, Denz F. Energy-oriented scheduling based on Evolutionary Algorithms. Comput Oper Res 2015;54:218-31.
3. Hu S, Liu F, He Y, Hu T. An on-line approach for energy efficiency monitoring of machine tools. J Clean Prod 2012;27:133-40.
4. Li JX, Wen XN. Construction and simulation of multi-objective rescheduling model based on PSO. Int j simul model 2020;19:323-33.
5. Yan Q, Wu W, Wang H. Deep reinforcement learning for distributed flow shop scheduling with flexible maintenance. Machines 2022;10:210.
6. Li T, Kong L, Zhang H, Iqbal A. Recent research and development of typical cutting machine tool’s energy consumption model. J Mech Eng 2014;50:102-11.
7. Zhang G, Xing K, Zhang G, He Z. Memetic algorithm with meta-lamarckian learning and simplex search for distributed flexible assembly permutation flowshop scheduling problem. IEEE Access 2020;8:96115-28.
8. Naderi B, Ruiz R. The distributed permutation flowshop scheduling problem. Comput Oper Res 2010;37:754-68.
9. Yang R, Sun X, Narasimhan K. A generalized algorithm for multi-objective reinforcement learning and policy adaptation. Available from: https://proceedings.neurips.cc/paper_files/paper/2019/file/4a46fbfca3f1465a27b210f4bdfe6ab3-Paper.pdf. [Last accessed on 14 Sep 2023]
10. Yang Y, Wang J. An overview of multi-agent reinforcement learning from game theoretical perspective. Available from: https://arxiv.org/abs/2011.00583. [Last accessed on 14 Sep 2023].
11. Aytug H, Lawley MA, Mckay K, Mohan S, Uzsoy R. Executing production schedules in the face of uncertainties: a review and some future directions. Eur J Oper Res 2005;161:86-110.
12. Zhang F, Mei Y, Nguyen S, Zhang M, Tan KC. Surrogate-assisted evolutionary multitask genetic programming for dynamic flexible job shop scheduling. IEEE Trans Evol Computat 2021;25:651-65.
13. Tawegoum R, Castelain E, Gentina J. Hierarchical and dynamic production control in flexible manufacturing systems. Robotics Comput Integr Manuf 1994;11:327-34.
14. Jawahar N, Aravindan P, Ponnambalam SG, Raghavendra LN. Knowledge-based workcell attribute oriented dynamic schedulers for flexible manufacturing systems. Int J Adv Manuf Technol 1998;14:514-38.
15. Braglia M, Petroni A. Data envelopment analysis for dispatching rule selection. Prod Plan Control 1999;10:454-61.
16. Elmaraghy HA, Elmekkawy TY. Deadlock-free rescheduling in flexible manufacturing systems. CIRP Annals 2002;51:371-4.
17. Chan FTS. Evaluation of combined dispatching and routeing strategies for a flexible manufacturing system. Proc Inst Mech Eng B J Eng Manuf 2002;216:1033-46.
18. Wang K, Choi SH. Solving stochastic flexible flow shop scheduling problems with a decomposition-based approach. AIP Conf Proc 2010;1247:374-88.
19. Weng W, Fujimura S. Distributed-intelligence approaches for weighted just-in-time production. IEEJ Trans Elec Electron Eng 2010;5:560-8.
20. Kianfar K, Fatemi Ghomi S, Oroojlooy Jadid A. Study of stochastic sequence-dependent flexible flow shop via developing a dispatching rule and a hybrid GA. Eng Appl Artif Intell 2012;25:494-506.
21. Abd K, Abhary K, Marian R. Simulation modelling and analysis of scheduling in robotic flexible assembly cells using Taguchi method. Int J Prod Res 2014;52:2654-66.
22. Hosseinabadi AAR, Siar H, Shamshirband S, Shojafar M, Nasir MHNM. Using the gravitational emulation local search algorithm to solve the multi-objective flexible dynamic job shop scheduling problem in small and medium enterprises. Ann Oper Res 2015;229:451-74.
23. Heger J, Branke J, Hildebrandt T, Scholz-reiter B. Dynamic adjustment of dispatching rule parameters in flow shops with sequence-dependent set-up times. Int J Prod Res 2016;54:6812-24.
24. Ivanov D, Dolgui A, Sokolov B, Werner F, Ivanova M. A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory industry 4.0. Int J Prod Res 2016;54:386-402.
25. Tang D, Dai M, Salido MA, Giret A. Energy-efficient dynamic scheduling for a flexible flow shop using an improved particle swarm optimization. Comput Ind 2016;81:82-95.
26. Rani M, Mathirajan M. Performance evaluation of due-date based dispatching rules in dynamic scheduling of diffusion furnace. OPSEARCH 2020;57:462-512.
27. Lei C, Zhao N, Ye S, Wu X. Memetic algorithm for solving flexible flow-shop scheduling problems with dynamic transport waiting times. Comput Ind Eng 2020;139:105984.
28. Luo S, Zhang L, Fan Y. Real-time scheduling for dynamic partial-no-wait multiobjective flexible job shop by deep reinforcement learning. IEEE Trans Automat Sci Eng 2022;19:3020-38.
29. Han B, Yang J. Research on adaptive job shop scheduling problems based on dueling double DQN. IEEE Access 2020;8:186474-95.
30. Gubernat S, Czarnota J, Masłoń A, Koszelnik P, Pękala A, Skwarczyńska-wojsa A. Efficiency of phosphorus removal and recovery from wastewater using marl and travertine and their thermally treated forms. J Water Process Eng 2023;53:103642.
31. Michalopoulou A, Markantonis I, Vlachogiannis D, Sfetsos A, Kilikoglou V, Karatasios I. Weathering mechanisms of porous marl stones in coastal environments and evaluation of conservation treatments as potential adaptation action for facing climate change impact. Buildings 2023;13:198.
32. Jiang S, Mokhtari M, Song J. Comparative study of elastic properties of marl and limestone layers in the Eagle Ford formation. Front Earth Sci 2023;10:1075151.
33. He C, Lin H. Improved algorithms for two-agent scheduling on an unbounded serial-batching machine. Discrete Optim 2021;41:100655.
34. Hepsiba PS, Kanaga EGM. An osmosis-based intelligent agent scheduling framework for cloud bursting in a hybrid cloud. Int J Distrib Syst Technol 2020;11:68-88.
35. Nicosia G, Pacifici A, Pferschy U. Competitive multi-agent scheduling with an iterative selection rule. 4OR-Q J Oper Res 2018;16:15-29.
36. Yuan H, Ni J, Hu J. A centralised training algorithm with D3QN for scalable regular unmanned ground vehicle formation maintenance. IET Intell Transp Syst 2021;15:562-72.
37. He Z, Tran KP, Thomassey S, Zeng X, Xu J, Yi C. Multi-objective optimization of the textile manufacturing process using deep-Q-network based multi-agent reinforcement learning. J Manuf Syst 2022;62:939-49.
