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Reinforcement learning for long-run average cost

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  • Gosavi, Abhijit

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  • Gosavi, Abhijit, 2004. "Reinforcement learning for long-run average cost," European Journal of Operational Research, Elsevier, vol. 155(3), pages 654-674, June.
    • Handle: RePEc:eee:ejores:v:155:y:2004:i:3:p:654-674
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    1. Cassandras, Christos G. & Han, Youngnam, 1992. "Optimal inspection policies for a manufacturing station," European Journal of Operational Research, Elsevier, vol. 63(1), pages 35-53, November.
    2. Shioyama, Tadayoshi, 1991. "Optimal control of a queuing network system with two types of customers," European Journal of Operational Research, Elsevier, vol. 52(3), pages 367-372, June.
    3. Tapas K. Das & Abhijit Gosavi & Sridhar Mahadevan & Nicholas Marchalleck, 1999. "Solving Semi-Markov Decision Problems Using Average Reward Reinforcement Learning," Management Science, INFORMS, vol. 45(4), pages 560-574, April.
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    Cited by:

    1. Stephane R. A. Barde & Soumaya Yacout & Hayong Shin, 2019. "Optimal preventive maintenance policy based on reinforcement learning of a fleet of military trucks," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 147-161, January.
    2. Jian Wang & Murtaza Das & Stephen Tappert, 2021. "Applying reinforcement learning to estimating apartment reference rents," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 20(3), pages 330-343, June.
    3. van Wezel, M.C. & van Eck, N.J.P., 2005. "Reinforcement learning and its application to Othello," Econometric Institute Research Papers EI 2005-47, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    4. Yang, Hongbing & Li, Wenchao & Wang, Bin, 2021. "Joint optimization of preventive maintenance and production scheduling for multi-state production systems based on reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    5. Schütz, Hans-Jörg & Kolisch, Rainer, 2012. "Approximate dynamic programming for capacity allocation in the service industry," European Journal of Operational Research, Elsevier, vol. 218(1), pages 239-250.
    6. Xiaonong Lu & Baoqun Yin & Haipeng Zhang, 2016. "A reinforcement-learning approach for admission control in distributed network service systems," Journal of Combinatorial Optimization, Springer, vol. 31(3), pages 1241-1268, April.
    7. Safaei, Fatemeh & Ahmadi, Jafar & Taghipour, Sharareh, 2022. "A maintenance policy for a k-out-of-n system under enhancing the system’s operating time and safety constraints, and selling the second-hand components," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    8. Li, Xueping & Wang, Jiao & Sawhney, Rapinder, 2012. "Reinforcement learning for joint pricing, lead-time and scheduling decisions in make-to-order systems," European Journal of Operational Research, Elsevier, vol. 221(1), pages 99-109.
    9. Singh, Sumeetpal S. & Tadic, Vladislav B. & Doucet, Arnaud, 2007. "A policy gradient method for semi-Markov decision processes with application to call admission control," European Journal of Operational Research, Elsevier, vol. 178(3), pages 808-818, May.
    10. Barlow, E. & Bedford, T. & Revie, M. & Tan, J. & Walls, L., 2021. "A performance-centred approach to optimising maintenance of complex systems," European Journal of Operational Research, Elsevier, vol. 292(2), pages 579-595.
    11. Peter Seele & Claus Dierksmeier & Reto Hofstetter & Mario D. Schultz, 2021. "Mapping the Ethicality of Algorithmic Pricing: A Review of Dynamic and Personalized Pricing," Journal of Business Ethics, Springer, vol. 170(4), pages 697-719, May.
    12. Duraikannan Sundaramoorthi & Victoria Chen & Jay Rosenberger & Seoung Kim & Deborah Buckley-Behan, 2010. "A data-integrated simulation-based optimization for assigning nurses to patient admissions," Health Care Management Science, Springer, vol. 13(3), pages 210-221, September.

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