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International Journal of Production Research Volume 54, 2016 - Issue 8
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Original Articles

Production control in a complex production system using approximate dynamic programming

Han WuDepartment of Industrial Engineering, University of Louisville, Louisville, KY, USA.
,
Gerald EvansDepartment of Industrial Engineering, University of Louisville, Louisville, KY, USA.
&
Ki-Hwan BaeDepartment of Industrial Engineering, University of Louisville, Louisville, KY, USA.Correspondence[email protected]
Pages 2419-2432 | Received 27 Feb 2015, Accepted 16 Aug 2015, Published online: 11 Sep 2015

Citations (8)

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Mustafa Çimen & Chris Kirkbride. (2017) Approximate dynamic programming algorithms for multidimensional flexible production-inventory problems. International Journal of Production Research 55:7, pages 2034-2050.
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Articles from other publishers (7)

Neelam Singh, Varsha Mittal, Vandana Rawat, Preeti Malik & Aayushi Rana. (2023) Research Trends in Supply Chain Management: A Machine Learning Perspective. Research Trends in Supply Chain Management: A Machine Learning Perspective.
Marcel Panzer, Benedict Bender & Norbert Gronau. (2022) Neural agent-based production planning and control: An architectural review. Journal of Manufacturing Systems 65, pages 743-766.
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Vipin Kumar, Harikumar Pallathadka, Sanjay Kumar Sharma, Chetan M. Thakar, Manisha Singh & Laxmi Kirana Pallathadka. (2022) Role of machine learning in green supply chain management and operations management. Materials Today: Proceedings 51, pages 2485-2489.
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Nihit Goyal, Ola G. El-Taliawi & Michael Howlett. 2022. Emerging Pedagogies for Policy Education. Emerging Pedagogies for Policy Education 99 123 .
Erfan Babaee Tirkolaee, Saeid Sadeghi, Farzaneh Mansoori Mooseloo, Hadi Rezaei Vandchali & Samira Aeini. (2021) Application of Machine Learning in Supply Chain Management: A Comprehensive Overview of the Main Areas. Mathematical Problems in Engineering 2021, pages 1-14.
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Xinyan Ou, Qing Chang & Nilanjan Chakraborty. (2021) A Method Integrating Q-Learning With Approximate Dynamic Programming for Gantry Work Cell Scheduling. IEEE Transactions on Automation Science and Engineering 18:1, pages 85-93.
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Du Ni, Zhi Xiao & Ming K. Lim. (2019) A systematic review of the research trends of machine learning in supply chain management. International Journal of Machine Learning and Cybernetics 11:7, pages 1463-1482.
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