Abstract
In order to mitigate food waste resulting from irrational pricing and concurrently augment overall business revenue, this paper introduces a finite-shelf-life food dynamic pricing framework denoted as W-DQN, founded upon the principles of deep reinforcement learning theory. Initially, the dynamic pricing problem for perishable goods is formulated as a Markov decision process. Subsequently, a dynamic pricing algorithm model and corresponding reward function are devised to bolster business revenue while curtailing food waste. Experimental findings unequivocally demonstrate that, relative to tabular-based dynamic pricing algorithm models, W-DQN attains commendable returns. Furthermore, the proposed reward function effectively reduces waste. In comparison to conventional pricing approaches, W-DQN significantly diminishes food waste, thereby enhancing overall business revenue.
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Acknowledgements
This research is supported by the Liaoning Provincial Department of Education Basic Research Project, the National Natural Science Foundation of China and the Doctoral Scientific Foundation of Liaoning Province of China under Grant (LJKQZ2021120, 61802041 and 2020-BS-210).
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Huang, H., Wang, C. (2024). Research on Food Dynamic Pricing Algorithm Based on Deep Reinforcement Learning. In: Song, H., Xu, M., Yang, L., Zhang, L., Yan, S. (eds) Innovative Technologies for Printing, Packaging and Digital Media. CACPP 2023. Lecture Notes in Electrical Engineering, vol 1144. Springer, Singapore. https://doi.org/10.1007/978-981-99-9955-2_63
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DOI: https://doi.org/10.1007/978-981-99-9955-2_63
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