Dynamic packaging in e-retailing with stochastic demand over finite horizons: A Q-learning approach

Expert Systems with Applications

Volumn 36, Issue 1, 2009, Pages 472-480

  Cheng, Yan ^a  

^a FUDAN UNIVERSITY   (China)

Author keywords

Dynamic packaging; E retailing; Q learning

Indexed keywords

STATE SPACE METHODS; STOCHASTIC PROGRAMMING;

DISTORTION MEASURES; DYNAMIC PACKAGING; E-RETAILING; EVENT-DRIVEN; FINITE HORIZONS; MARKOV DECISION PROCESS; Q-LEARNING; Q-LEARNING APPROACH; Q-VALUES; SIMULATION-BASED; STATE SPACES; STATE-DEPENDENT; STOCHASTIC DEMANDS;

EDUCATION;

EID: 53849147885     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2007.09.050     Document Type: Article

References (20)

1. Baldwin M. Dynamic packaging. Travel Agent 321 5 (2005) 76-78
2. Cardoso J. Developing dynamic packaging systems using semantic web technologies. Transactions on Information Science and Applications 3 4 (2006) 729-736
3. Chapman, D., & Kaelbling, L. P. (1995). Input generalization in delayed reinforcement learning: An algorithm and performance comparisons. In Proceedings of the 15th international conference on machine learning (pp. 136-143).
4. Chau M., Zeng D., Chen H., Huang M., and Hendriawan D. Design and evaluation of a multi-agent collaborative web mining system. Decision Support Systems 35 (2003) 167-183
5. Hailu G. Symbolic structures in numeric reinforcement for learning optimum robot trajectory. Robotics and Autonomous Systems 37 (2001) 53-68
6. Jaakkola T., Jordan M.I., and Singh S.P. On the convergence of stochastic iterative dynamic programming algorithms. Neural Computation 6 (1994) 1185-1201
7. Lin L.-J. Self-improving reactive agents based on reinforcement learning, planning and teaching. Machine Learning 8 3/4 (1992) 293-321
8. Mahavedan M., and Connell J. Automatic programming of behavior-based robots using reinforcement learning. Artificial Intelligence 55 (1992) 311-365
9. Millán J.R., Posenato D., and Dedieu E. Continuous-action Q-learning. Machine Learning 49 (2002) 247-265
10. Moe W.W., and Fader P.S. Dynamic conversion behavior at e-commerce sites. Management Science 50 (2004) 326-335
11. Moreno, D. L., Regueiro, C. V., Iglesias, R., et al. (2004). Using prior knowledge to improve reinforcement learning in mobile robotics. In Proceedings of the STAROS 2004, "Toward autonomous robotic systems".
12. Netessine S., Savin S., and Xiao W. Revenue management through dynamic cross selling in e-commerce retailing. Operation Research 54 5 (2006) 893-913
13. Schafer J.B., Konstan J.A., and Riedl J. E-commerce recommendation applications. Data Mining and Knowledge Discovery 5 (2001) 115-153
14. Schmeing, T., Cardoso, J., & Fernandes, J. D. (2006). Knowledge-based dynamic packaging model. In Proceedings of IEEE international conference on management of innovation and technology (pp. 1085-1089).
15. Sehad, S., & Touzet, C. (1994). Self-organising map for reinforcement learning: Obstacle avoidance with khepera. In Proceedings of from perception to action. Lausanne, Switzerland: IEEE Computer Society Press.
16. Smith A.J. Application of self-organising map to reinforcement learning. Neural Networks 15 (2002) 1107-1124
17. Suh E., Lim S., Hwang H., and Kim S. A prediction model for the purchase probability of anonymous customers to support real time web marketing. Expert Systems with Applications 27 (2004) 245-255
18. Touzet C. Neural reinforcement learning for behaviour synthesis. Robotics and autonomous systems. Special Issue on Learning Robots: The New Wave 22 (1997) 251-281
19. Watkins C., and Dayan P. Q-learning. Machine Learning 8 (1992) 279-292
20. Wedel, J., & Polani, D. (1996). Critic-based learning of actions with selforganising feature maps. Technical Report 5/96. Institute for Informatics, Johannes-Gutenberg University.