References
- Arica, Emrah, Cecilia Haskins, and Jan O. Strandhagen. 2016. “A Framework for Production Rescheduling in Sociotechnical Manufacturing Environments.” Production Planning and Control 27 (14): 1191–1205. doi:https://doi.org/10.1080/09537287.2016.1193910.
- Aytug, Haldun, Mark A. Lawley, Kenneth McKay, Shantha Mohan, and Reha Uzsoy. 2005. “Executing Production Schedules in the Face of Uncertainties: A Review and Some Future Directions.” European Journal of Operational Research 161 (1): 86–110. doi:https://doi.org/10.1016/j.ejor.2003.08.027.
- Bannat, Alexander, Thibault Bautze, Michael Beetz, Juergen Blume, Klaus Diepold, Christoph Ertelt, Florian Geiger, et al. 2011. “Artificial Cognition in Production Systems.” IEEE Transactions on Automation Science and Engineering 8 (1): 148–174. doi:https://doi.org/10.1109/TASE.2010.2053534.
- Bauer, A., R. Rowden, J. Browne, J. Duggan, and G. Lyons. 1991. Shop Floor Control Systems: From Design to Implementation. Edited by Chapman & Hall. London.
- Belz, R., and P. Mertens. 1996. “Combining Knowledge-Based Systems and Simulation to Solve Rescheduling Problems.” Decision Support Systems 17 (2): 141–157. doi:https://doi.org/10.1016/0167-9236(95)00029-1.
- Bonami, Pierre, Lorenz T. Biegler, Andrew R. Conn, Gérard Cornuéjols, Ignacio E. Grossmann, Carl D. Laird, Jon Lee, et al. 2008. “An Algorithmic Framework for Convex Mixed Integer Nonlinear Programs.” Discrete Optimization 5 (2): 186–204. doi:https://doi.org/10.1016/j.disopt.2006.10.011.
- Bouri, Ahmed El, and Gholam R Amin. 2015. “Computers & Industrial Engineering a Combined OWA – DEA Method for Dispatching Rule Selection.” Computers & Industrial Engineering 88: 470–478. doi:https://doi.org/10.1016/j.cie.2015.08.007.
- Chen, Xili, Xinchang Hao, Hao Wen Lin, and Tomohiro Murata. 2010. “Rule Driven Multi Objective Dynamic Scheduling by Data Envelopment Analysis and Reinforcement Learning.” 2010 IEEE International Conference on Automation and Logistics, ICAL, 396–401. doi:https://doi.org/10.1109/ICAL.2010.5585316.
- Chen, C. C., and Y. Yih. 1996. “Indentifying Attributes for Knowledge-Based Development in Dynamic Scheduling Environments.” International Journal of Production Research 34 (6): 1739–1755. doi:https://doi.org/10.1080/00207549608904994.
- Csáji, Balázs Csanád, László Monostori, and Botond Kádár. 2006. “Reinforcement Learning in a Distributed Market-Based Production Control System.” Advanced Engineering Informatics 20 (3): 279–288. doi:https://doi.org/10.1016/j.aei.2006.01.001.
- Cui, Jian, and Sebastian Engell. 2010. “Medium-Term Planning of a Multiproduct Batch Plant under Evolving Multi-Period Multi-Uncertainty by Means of a Moving Horizon Strategy.” Computers and Chemical Engineering 34 (5): 598–619. doi:https://doi.org/10.1016/j.compchemeng.2010.01.013.
- De Ugarte, B. Saenz, A. Artiba, and R. Pellerin. 2009. “Manufacturing Execution System – a Literature Review.” Production Planning and Control 20 (6): 525–539. doi:https://doi.org/10.1080/09537280902938613.
- Dorn, Jürgen, Roger Kerr, and Gabi Thalhammer. 1995. “Reactive Scheduling: Improving the Robustness of Schedules and Restricting the Effects of Shop Floor Disturbances by Fuzzy Reasoning.” International Journal of Human-Computer Studies 42 (6): 687–704. doi:https://doi.org/10.1006/ijhc.1995.1031.
- Ðurasević, Marko, and Domagoj Jakobović. 2018. “A Survey of Dispatching Rules for the Dynamic Unrelated Machines Environment.” Expert Systems with Applications 113: 555–569. doi:https://doi.org/10.1016/j.eswa.2018.06.053.
- El-bouri, Ahmed. 2012. “Computers & Operations Research A Cooperative Dispatching Approach for Minimizing Mean Tardiness in a Dynamic Flowshop.” Computers and Operation Research 39 (7): 1305–1314. doi:https://doi.org/10.1016/j.cor.2011.07.004.
- Framinan, Jose M., Victor Fernandez-Viagas, and Paz Perez-Gonzalez. 2019. “Using Real-Time Information to Reschedule Jobs in a Flowshop with Variable Processing Times.” Computers and Industrial Engineering 129: 113–125. doi:https://doi.org/10.1016/j.cie.2019.01.036.
- Frantzén, Marcus, Amos H.C. Ng, and Philip Moore. 2011. “A Simulation-Based Scheduling System for Real-Time Optimization and Decision Making Support*.” Robotics and Computer-Integrated Manufacturing 27 (4): 696–705. doi:https://doi.org/10.1016/j.rcim.2010.12.006.
- Frye, Maik, Dávid Gyulai, Júlia Bergmann, and Robert H. Schmitt. 2021. “Production Rescheduling Through Product Quality Prediction.” Procedia Manufacturing 54: 142–147. doi:https://doi.org/10.1016/j.promfg.2021.07.022.
- Gao, Kai Zhou, Ponnuthurai Nagaratnam Suganthan, Tay Jin Chua, Chin Soon Chong, Tian Xiang Cai, and Qan Ke Pan. 2015. “A Two-Stage Artificial Bee Colony Algorithm Scheduling Flexible Job-Shop Scheduling Problem with New Job Insertion.” Expert Systems with Applications 42 (21): 7652–7663. doi:https://doi.org/10.1016/j.eswa.2015.06.004.
- Garner, B. J., and G. J. Ridley. 1993. “Application of Neural Network Process Models in Reactive Scheduling.” Proceedings of the IFIP TC5/WG5. 7 International Workshop on Knowledge-Based Reactive Scheduling, 19–28. North-Holland.
- Gersmann, Kai, and Barbara Hammer. 2005. “Improving Iterative Repair Strategies for Scheduling with the SVM.” Neurocomputing 63 (SPEC. ISS.): 271–292. doi:https://doi.org/10.1016/j.neucom.2004.01.193.
- Ghaleb, Mageed, Hossein Zolfagharinia, and Sharareh Taghipour. 2020. “Real-Time Production Scheduling in the Industry-4.0 Context: Addressing Uncertainties in Job Arrivals and Machine Breakdowns.” Computers and Operations Research 123: 105031. doi:https://doi.org/10.1016/j.cor.2020.105031.
- Gomes, Marta Castilho, Ana Paula Barbosa-Póvoa, and Augusto Queiroz Novais. 2013. “Reactive Scheduling in a Make-to-Order Flexible Job Shop with Re-Entrant Process and Assembly: A Mathematical Programming Approach.” International Journal of Production Research 51 (17): 5120–5141. doi:https://doi.org/10.1080/00207543.2013.793428.
- Green, Gary I., and Leonard B. Appel. 1981. “An Empirical Analysis of Job Shop Dispatch Rule Selection.” Journal of Operations Management 1 (4): 197–203. doi:https://doi.org/10.1016/0272-6963(81)90025-5.
- Guez, Arthur, Mehdi Mirza, Karol Gregor, Rishabh Kabra, Sébastien Racanière, Théophane Weber, David Raposo, et al. 2019. “An Investigation of Model-Free Planning.”
- Gupta, Dhruv, Christos T. Maravelias, and John M. Wassick. 2016. “From Rescheduling to Online Scheduling.” Chemical Engineering Research and Design 116: 83–97. doi:https://doi.org/10.1016/j.cherd.2016.10.035.
- Harjunkoski, Iiro, Christos T. Maravelias, Peter Bongers, Pedro M. Castro, Sebastian Engell, Ignacio E. Grossmann, John Hooker, Carlos Méndez, Guido Sand, and John Wassick. 2014. “Scope for Industrial Applications of Production Scheduling Models and Solution Methods.” Computers and Chemical Engineering 62: 161–193. doi:https://doi.org/10.1016/j.compchemeng.2013.12.001.
- Harjunkoski, Iiro, Rasmus Nyström, and Alexander Horch. 2009. “Integration of Scheduling and Control-Theory or Practice?” Computers and Chemical Engineering 33 (12): 1909–1918. doi:https://doi.org/10.1016/j.compchemeng.2009.06.016.
- Heever, Susara A. Van Den, and Ignacio E. Grossmann. 2003. “A Strategy for the Integration of Production Planning and Reactive Scheduling in the Optimization of a Hydrogen Supply Network.” Computers and Chemical Engineering 27 (12): 1813–1839. doi:https://doi.org/10.1016/S0098-1354(03)00158-3.
- Heger, Jens, Jürgen Branke, Torsten Hildebrandt, and Bernd Scholz-reiter. 2016. “Dynamic Adjustment of Dispatching Rule Parameters in Flow Shops with Sequence- Dependent Set-up Times” 7543 (May): 0–13. doi:https://doi.org/10.1080/00207543.2016.1178406.
- Henning, Gabriela P. 2009. “Production Scheduling in the Process Industries: Current Trends, Emerging Challenges and Opportunities.” Computer Aided Chemical Engineering 27: 23–28. doi:https://doi.org/10.1016/S1570-7946(09)70224-X.
- Honkomp, S. J., S. Lombardo, O. Rosen, and J. F. Pekny. 2000. “The Curse of Reality – Why Process Scheduling Optimization Problems Are Difficult in Practice.” Computers and Chemical Engineering 24 (2–7): 323–328. doi:https://doi.org/10.1016/S0098-1354(00)00468-3.
- Huang, Chieh-Sen, Yi-Chen Huang, and Peng-Jen Lai. 2012. “Modified Genetic Algorithms for Solving Fuzzy Flow Shop Scheduling Problems and Their Implementation with CUDA.” Expert Systems with Applications 39 (5): 4999–5005. doi:https://doi.org/10.1016/j.eswa.2011.10.013.
- Hubbs, Christian D., Can Li, Nikolaos V. Sahinidis, Ignacio E. Grossmann, and John M. Wassick. 2020. “A Deep Reinforcement Learning Approach for Chemical Production Scheduling.” Computers and Chemical Engineering 141: 106982. doi:https://doi.org/10.1016/j.compchemeng.2020.106982.
- Ikonen, Teemu J., Keijo Heljanko, and Iiro Harjunkoski. 2020. “Reinforcement Learning of Adaptive Online Rescheduling Timing and Computing Time Allocation.” Computers and Chemical Engineering 141: 106994. doi:https://doi.org/10.1016/j.compchemeng.2020.106994.
- Ivert, Linea Kjellsdotter. 2012. “Shop Floor Characteristics Influencing the Use of Advanced Planning and Scheduling Systems.” Production Planning & Control 23 (6): 452–467. doi:https://doi.org/10.1080/09537287.2011.564218.
- Jahangirian, M., and G. V. Conroy. 2000. “Intelligent Dynamic Scheduling System: The Application of Genetic Algorithms.” Integrated Manufacturing Systems 11 (4): 247–257.
- Janak, Stacy L., Christodoulos A. Floudas, Josef Kallrath, and Norbert Vormbrock. 2006. “Production Scheduling of a Large-Scale Industrial Batch Plant. II. Reactive Scheduling.” Industrial and Engineering Chemistry Research 45 (25): 8253–8269. doi:https://doi.org/10.1021/ie0600590.
- Kim, Haejoong, Dae Eun Lim, and Sangmin Lee. 2020. “Deep Learning-Based Dynamic Scheduling for Semiconductor Manufacturing with High Uncertainty of Automated Material Handling System Capability.” IEEE Transactions on Semiconductor Manufacturing 33 (1): 13–22. doi:https://doi.org/10.1109/TSM.2020.2965293.
- Kim, Jinhan, and Shin Yoo. 2019. “Software Review: DEAP (Distributed Evolutionary Algorithm in Python) Library.” Genetic Programming and Evolvable Machines 20 (1): 139–142. doi:https://doi.org/10.1007/s10710-018-9341-4.
- Kingma, Diederik P., and Jimmy Ba. 2014. “Adam: A Method for Stochastic Optimization.” ArXiv Preprint ArXiv:1412.6980.
- Kopanos, Georgios M., Elisabet Capón-García, Antonio Espuña, and Luis Puigjaner. 2008. “Costs for Rescheduling Actions: A Critical Issue for Reducing the Gap Between Scheduling Theory and Practice.” Industrial and Engineering Chemistry Research 47 (22): 8785–8795. doi:https://doi.org/10.1021/ie8005676.
- Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E Hinton. 2012. “ImageNet Classification with Deep Convolutional Neural Networks.” In Advances in Neural Information Processing Systems 25, edited by F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, 1097–1105. Curran Associates. https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf.
- LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. 2015. “Deep Learning.” Nature 521 (May): 436. doi:https://doi.org/10.1038/nature14539.
- Lee, G.-C., Y.-D. Kim, J.-G. Kim, and S.-H. Choi. 2003. “A Dispatching Rule-Based Approach to Production Scheduling in a Printed Circuit Board Manufacturing System.” Journal of the Operational Research Society 54 (10): 1038–1049. doi:https://doi.org/10.1057/palgrave.jors.2601601.
- Li, Yuanyuan, Stefano Carabelli, Edoardo Fadda, Daniele Manerba, Roberto Tadei, and Olivier Terzo. 2020. “Machine Learning and Optimization for Production Rescheduling in Industry 4.0.”.
- Li, Zukui, and Marianthi G. Ierapetritou. 2008. “Reactive Scheduling Using Parametric Programming.” AIChE Journal 54 (10): 2610–2623. doi:https://doi.org/10.1002/aic.11593.
- Li, Heng, Zhicheng Li, Ling X. Li, and Bin Hu. 2000. “A Production Rescheduling Expert Simulation System.” European Journal of Operational Research 124 (2): 283–293. doi:https://doi.org/10.1016/S0377-2217(99)00381-1.
- Li, Xixing, Zhao Peng, Baigang Du, Jun Guo, Wenxiang Xu, and Kejia Zhuang. 2017. “Hybrid Artificial Bee Colony Algorithm with a Rescheduling Strategy for Solving Flexible Job Shop Scheduling Problems.” Computers and Industrial Engineering 113: 10–26. doi:https://doi.org/10.1016/j.cie.2017.09.005.
- Liao, Yongxin, Fernando Deschamps, Eduardo de Freitas Rocha Loures, and Luiz Felipe Pierin Ramos. 2017. “Past, Present and Future of Industry 4.0 – a Systematic Literature Review and Research Agenda Proposal.” International Journal of Production Research 55 (12): 3609–3629. doi:https://doi.org/10.1080/00207543.2017.1308576.
- Liu, Chien Liang, Chuan Chin Chang, and Chun Jan Tseng. 2020. “Actor-Critic Deep Reinforcement Learning for Solving Job Shop Scheduling Problems.” IEEE Access 8: 71752–71762. doi:https://doi.org/10.1109/ACCESS.2020.2987820.
- Liu, Feng, Shengbin Wang, Yuan Hong, and Xiaohang Yue. 2017. “On the Robust and Stable Flowshop Scheduling Under Stochastic and Dynamic Disruptions.” 1–15.
- Maier, Paul, Martin Sachenbacher, Thomas Rühr, and Lukas Kuhn. 2010. “Automated Plan Assessment in Cognitive Manufacturing.” Advanced Engineering Informatics 24 (3): 308–319. doi:https://doi.org/10.1016/j.aei.2010.05.015.
- Malapert, Arnaud, Christelle Guéret, and Louis Martin Rousseau. 2012. “A Constraint Programming Approach for a Batch Processing Problem with Non-Identical Job Sizes.” European Journal of Operational Research 221 (3): 533–545. doi:https://doi.org/10.1016/j.ejor.2012.04.008.
- McKay, Kenneth, and Vincent C. S. Wiers. 2001. “Decision Support for Production Scheduling Tasks in Shops with Much Uncertainty and Little Autonomous Flexibility.” In Human Performance in Planning and Scheduling, edited by L. MacCarthy and J. R. Wilson, 167–179. London: Taylor & Francis.
- Méndez, Carlos A., and Jaime Cerdá. 2003. “Dynamic Scheduling in Multiproduct Batch Plants.” Computers and Chemical Engineering 27 (8–9): 1247–1259. doi:https://doi.org/10.1016/S0098-1354(03)00050-4.
- Mendez, Carlos A., and Jaime Cerdá. 2004. “An MILP Framework for Batch Reactive Scheduling with Limited Discrete Resources.” Computers and Chemical Engineering 28: 1059–1068. doi:https://doi.org/10.1016/j.compchemeng.2003.09.008.
- Meyer, Gerben G, J. C. Hans Wortmann, and Nick B Szirbik. 2011. “Production Monitoring and Control with Intelligent Products.” International Journal of Production Research 49 (5): 1303–1317. doi:https://doi.org/10.1080/00207543.2010.518742.
- Miyashita, K. 2000. “Learning Scheduling Control Knowledge Through Reinforcements.” International Transactions in Operational Research 7 (2): 125–138. doi:https://doi.org/10.1016/S0969-6016(00)00014-9.
- Miyashita, Kazuo, and Katia Sycara. 1995. “CABINS: A Framework of Knowledge Acquisition and Iterative Revision for Schedule Improvement and Reactive Repair.” Artificial Intelligence 76 (1–2): 377–426. doi:https://doi.org/10.1016/0004-3702(94)00089-J.
- Mnih, Volodymyr, Adrià Puigdoménech Badia, Mehdi Mirza, Alex Graves, Tim Harley, Timothy P Lillicrap, David Silver, and Koray Kavukcuoglu. 2016. “Asynchronous Methods for Deep Reinforcement Learning.” Proceedings of the 33rd International Conference on International Conference on Machine Learning – Volume 48, 1928–1937. ICML’16. JMLR.org.
- Mnih, Volodymyr, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, et al. 2015. “Human-Level Control Through Deep Reinforcement Learning.” Nature 518 (7540): 529–533. doi:https://doi.org/10.1038/nature14236.
- Nair, V., and G. E. Hinton. 2010. “Rectified Linear Units Improve Restricted Boltzmann Machines.” Proceedings of the 27th International Conference on Machine Learning. https://doi.org/https://doi.org/10.11165/6419.
- Novas, Juan M., and Gabriela P. Henning. 2010. “Reactive Scheduling Framework Based on Domain Knowledge and Constraint Programming.” Computers and Chemical Engineering 34 (12): 2129–2148. doi:https://doi.org/10.1016/j.compchemeng.2010.07.011.
- O’Kane, James F. 2000. “Knowledge-Based System for Reactive Scheduling Decision-Making in FMS.” Journal of Intelligent Manufacturing 11 (5): 461–474. doi:https://doi.org/10.1023/A:1008970213581.
- Ouelhadj, Djamila, and Sanja Petrovic. 2009. “A Survey of Dynamic Scheduling in Manufacturing Systems.” Journal of Scheduling 12 (4): 417–431. doi:https://doi.org/10.1007/s10951-008-0090-8.
- Palombarini, J., and E. Martínez. 2012a. “SmartGantt – An Intelligent System for Real Time Rescheduling Based on Relational Reinforcement Learning.” Expert Systems with Applications 39: 11. doi:https://doi.org/10.1016/j.eswa.2012.02.176.
- Palombarini, J., and E. Martínez. 2012b. “SmartGantt – An Interactive System for Generating and Updating Rescheduling Knowledge Using Relational Abstractions.” Computers and Chemical Engineering 47, doi:https://doi.org/10.1016/j.compchemeng.2012.06.021.
- Palombarini, Jorge, and Ernesto Martínez. 2020. “Training-Test Source Code.” https://github.com/jpu2/rescheduling_agent.
- Palombarini, Jorge, and Ernesto Martínez. 2021. “Data and Results for Paper End-to-End on-Line Rescheduling from Gantt Chart Images Using Deep Reinforcement Learning.” https://doi.org/https://doi.org/10.17632/x9vdrdwyfh.1.
- Panwalkar, S. S., and Wafik Iskander. 1977. “A Survey of Scheduling Rules.” Operations Research 25 (1): 45–61. doi:https://doi.org/10.1287/opre.25.1.45.
- Petrovic, D., and Alejandra Duenas. 2006. “A Fuzzy Logic Based Production Scheduling/Rescheduling in the Presence of Uncertain Disruptions.” Fuzzy Sets and Systems 157 (16): 2273–2285. doi:https://doi.org/10.1016/j.fss.2006.04.009.
- Petrovic, S., D. Petrovic, and E. Burke. 2011. “Fuzzy Logic-Based Production Scheduling and Rescheduling in the Presence of Uncertainty.” In Planning Production and Inventories in the Extended Enterprise. International Series in Operations Research & Management Science, edited by K. Kempf, P. Keskinocak, and R. Uzsoy. New York: Springer. doi:https://doi.org/10.1007/978-1-4419-8191-2_20.
- Rangsaritratsamee, Ruedee, William G. Ferrell, and Mary Beth Kurz. 2004. “Dynamic Rescheduling That Simultaneously Considers Efficiency and Stability.” Computers and Industrial Engineering 46 (1): 1–15. doi:https://doi.org/10.1016/j.cie.2003.09.007.
- Relvas, Susana, Henrique A. Matos, Ana Paula F. D. Barbosa-Póvoa, and João Fialho. 2007. “Reactive Scheduling Framework for a Multiproduct Pipeline with Inventory Management.” Industrial and Engineering Chemistry Research 46 (17): 5659–5672. doi:https://doi.org/10.1021/ie070214q.
- Rodrigues, M. T. M., L. Gimeno, C. A. S. Passos, and M. D. Campos. 1996. “Reactive Scheduling Approach for Multipurpose Chemical Batch Plants.” Computers and Chemical Engineering 20 (SUPPL.2): 1215–1220. doi:https://doi.org/10.1016/0098-1354(96)00210-4.
- Roslöf, J., I. Harjunkoski, J. Björkqvist, S. Karlsson, and T. Westerlund. 2000. “An MILP-Based Reordering Algorithm for Complex Industrial Scheduling and Rescheduling.” Computer Aided Chemical Engineering 8 (C): 13–18. doi:https://doi.org/10.1016/S1570-7946(00)80004-8.
- Rossit, Daniel Alejandro, Fernando Tohmé, and Mariano Frutos. 2019. “Industry 4.0: Smart Scheduling.” International Journal of Production Research 57 (12): 3802–3813. doi:https://doi.org/10.1080/00207543.2018.1504248.
- Ryu, Jun-hyung, Vivek Dua, and Efstratios N Pistikopoulos. 2007. “Proactive Scheduling under Uncertainty: A Parametric Optimization Approach.” Industrial & Engineering Chemistry Research 46 (24): 8044–8049. doi:https://doi.org/10.1021/ie070018j.
- Salido, Miguel A., Joan Escamilla, Federico Barber, and Adriana Giret. 2017. “Rescheduling in Job-Shop Problems for Sustainable Manufacturing Systems.” Journal of Cleaner Production 162: S121–S132. doi:https://doi.org/10.1016/j.jclepro.2016.11.002.
- Sand, G., and S. Engell. 2004. “Modeling and Solving Real-Time Scheduling Problems by Stochastic Integer Programming.” Computers and Chemical Engineering 28 (6–7): 1087–1103. doi:https://doi.org/10.1016/j.compchemeng.2003.09.009.
- Sand, G., J. Till, T. Tometzki, M. Urselmann, S. Engell, and M. Emmerich. 2008. “Engineered Versus Standard Evolutionary Algorithms: A Case Study in Batch Scheduling with Recourse.” Computers and Chemical Engineering 32 (11): 2706–2722. doi:https://doi.org/10.1016/j.compchemeng.2007.09.006.
- Schulman, John, Philipp Moritz, Sergey Levine, Michael I. Jordan, and Pieter Abbeel. 2016. “Proceedings of the 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings.
- Silver, David, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, and Thomas Hubert. 2017. “Mastering the Game of Go without Human Knowledge.” Nature 550 (7676).
- Subramanian, Kaushik, Christos T. Maravelias, and James B. Rawlings. 2012. “A State-Space Model for Chemical Production Scheduling.” Computers and Chemical Engineering 47: 97–110. doi:https://doi.org/10.1016/j.compchemeng.2012.06.025.
- Sutskever, Ilya, Oriol Vinyals, and Quoc V Le. 2014. “Sequence to Sequence Learning with Neural Networks.” In Advances in Neural Information Processing Systems 27, edited by Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger, 3104–3112. Curran Associates. https://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf.
- Sutton, Richard S., and Andrew G. Barto. 2018. Reinforcement Learning: An Introduction. Cambridge: MIT Press.
- Tian, Songling, Taiyong Wang, Lei Zhang, and Xiaoqiang Wu. 2019. “Real-Time Shop Floor Scheduling Method Based on Virtual Queue Adaptive Control : Algorithm and Experimental Results.” Measurement 147: 106689. doi:https://doi.org/10.1016/j.measurement.2019.05.080.
- Tighazoui, Ayoub, Christophe Sauvey, and Nathalie Sauer. 2021a. “Minimizing the Total Weighted Waiting Times and Instability in a Rescheduling Problem with Dynamic Jobs Weight.” Applied Sciences 11 (15): 7040. doi:https://doi.org/10.3390/APP11157040.
- Tighazoui, Ayoub, Christophe Sauvey, and Nathalie Sauer. 2021b. “Predictive-Reactive Strategy for Identical Parallel Machine Rescheduling.” Computers & Operations Research 134 (October): 105372. doi:https://doi.org/10.1016/J.COR.2021.105372.
- Trentesaux, Damien. 2009. “Distributed Control of Production Systems.” Engineering Applications of Artificial Intelligence 22 (7): 971–978. doi:https://doi.org/10.1016/j.engappai.2009.05.001.
- Vieira, Guilherme E, Jeffrey W Herrmann, and Edward Lin. 2003. “Resheduling Manufacturing System : A Framework of Strategies, Policies, and Methods.” Jounal of Scheduling 6 (1): 39–62.
- Virtanen, Pauli, Ralf Gommers, Travis E Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, et al. 2020. “(SciPy) 1.0: Fundamental Algorithms for Scientific Computing in Python.” Nature Methods 17: 261–272. doi:https://doi.org/10.1038/s41592-019-0686-2.
- Wang, Yi Chi, and John M. Usher. 2005. “Application of Reinforcement Learning for Agent-Based Production Scheduling.” Engineering Applications of Artificial Intelligence 18 (1): 73–82. doi:https://doi.org/10.1016/j.engappai.2004.08.018.
- Waschneck, Bernd, André Reichstaller, Lenz Belzner, Thomas Altenmüller, Thomas Bauernhansl, Alexander Knapp, and Andreas Kyek. 2018. “Optimization of Global Production Scheduling with Deep Reinforcement Learning.” Procedia CIRP 72: 1264–1269. doi:https://doi.org/10.1016/j.procir.2018.03.212.
- Webster, Scott. 2000. “A Case Study of Scheduling Practice at a Machine Tool Manufacturer.” In Human Performance in Planning and Scheduling, edited by L. MacCarthy and J. R. Wilson, 67–81. London: Taylor & Francis.
- Wiers, V. C. S. 2009. “The Relationship Between Shop Floor Autonomy and APS Implementation Success: Evidence from Two Cases.” Production Planning and Control 20 (7): 576–585. doi:https://doi.org/10.1080/09537280903034289.
- Xanthopoulos, A. S., D. E. Koulouriotis, A. Gasteratos, and S. Ioannidis. 2016. “Efficient Priority Rules for Dynamic Sequencing with Sequence-Dependent Setups.” International Journal of Industrial Engineering Computations 7 (3), doi:https://doi.org/10.5267/j.ijiec.2016.2.002.
- Yagmahan, Betul, and Mehmet Mutlu Yenisey. 2010. “A Multi-Objective Ant Colony System Algorithm for Flow Shop Scheduling Problem.” Expert Systems with Applications 37 (2): 1361–1368. doi:https://doi.org/10.1016/j.eswa.2009.06.105.
- Yamakawa, Hiroshi. 2020. “Attentional Reinforcement Learning in the Brain.” New Generation Computing 38 (1): 49–64. doi:https://doi.org/10.1007/s00354-019-00081-z.
- Ying, K.-C. 2009. “An Iterated Greedy Heuristic for Multistage Hybrid Flowshop Scheduling Problems with Multiprocessor Tasks.” Journal of the Operational Research Society 60 (6): 810–817. doi:https://doi.org/10.1057/palgrave.jors.2602625.
- Zaeh, Michael F., Gunther Reinhart, Martin Ostgathe, Florian Geiger, and Christian Lau. 2010a. “A Holistic Approach for the Cognitive Control of Production Systems.” Advanced Engineering Informatics 24 (3): 300–307. doi:https://doi.org/10.1016/j.aei.2010.05.014.
- Zaeh, Michael F., Gunther Reinhart, Martin Ostgathe, Florian Geiger, and Christian Lau. 2010b. “A Holistic Approach for the Cognitive Control of Production Systems.” Advanced Engineering Informatics 24 (3): 300–307. doi:https://doi.org/10.1016/j.aei.2010.05.014.
- Zakaria, Zalmiyah, and Sanja Petrovic. 2012. “Genetic Algorithms for Match-up Rescheduling of the Flexible Manufacturing Systems.” Computers and Industrial Engineering 62 (2): 670–686. doi:https://doi.org/10.1016/j.cie.2011.12.001.
- Zhang, Wei, and Thomas G Dietterich. 1996. “High-Performance Job-Shop Scheduling With A Time-Delay TD(Lambda) Network.” Advances in Neural Information Processing Systems 8: 1024–1030.
- Zhang, Jie, Junliang Wang, and Wei Qin. 2016. “Artificial Neural Networks in Production Scheduling and Yield Prediction of Semiconductor Wafer Fabrication System.” Artificial Neural Networks – Models and Applications, 356–390. doi:https://doi.org/10.5772/63444.