Proceedings of the 15th International Conference on Management, Economics and Humanities
Year: 2024
DOI:
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Dynamic Recycling-point System Using Deep Reinforcement Learning
Masahiro Sato
ABSTRACT:
This presentation introduces our ongoing experimental project of the Dynamic Recycling-point System. Recycling-point systems are the practices operated by private recycling companies in many cities in Japan. They issue recycling points to users who bring recyclable wastes such as wastepaper or plastic bottles to recycling stations installed in supermarkets or shopping malls. The points are exchangeable for shop coupons. Normally, the amount of the points given to a user is proportional to the weight of the waste he/she brings at a fixed rate. The users’ supply of wastes, however, can change dynamically depending on various factors such as shopping habits or weather conditions, and potentially on the changing rate of the recycling-point. Us project, the Dynamic Recycling-point System, is the first attempt in Japan that introduces a concept of dynamic pricing to recycling-point systems. In our system, the point rate is determined and announced a week in advance by a deep-reinforcement-learning agent considering the days-of-week, holidays, months, weather forecasts, and the rates of the six days before the target date. In this presentation, the presenter explains the detailed architecture of the system and provides an interim analysis, where the users turn out to respond to the dynamics of the generated point and days-of-week most strongly.
keywords: circular economy; consumer; deep learning; dynamic pricing; wastepaper