Due to the growing volume of e-waste in the world and its environmental impact, it is important to understand how to extend the useful life of electronic items. In this paper, we examine the remanufacturing process of end-of-life laptops for third-party remanufacturers and consider their pricing problem, which involves issues like a lack of reliable datasets, fluctuating costs of new components, and difficulties in benchmarking laptop prices, to name a few. We develop a unique approach that uses machine learning algorithms to help price remanufactured laptops. Our methodology involves a variety of techniques, which include an additive model, CART analysis, Random Forest, and Polynomial Regression. We consider depreciation and discount factors to account for the varying ages and conditions of laptops when estimating remanufactured laptop prices. Finally, we also compare our estimated prices to traditional prices. In summary, we leverage data-driven decision-making and develop a robust methodology for pricing remanufactured laptops to extend their lifespan.

由于世界上电子垃圾数量不断增加及其对环境的影响，了解如何延长电子产品的使用寿命非常重要。在本文中，我们研究了第三方再制造商报废笔记本电脑的再制造过程，并考虑了他们的定价问题，其中涉及缺乏可靠数据集、新组件成本波动以及笔记本电脑价格基准测试困难等问题，仅举几例。我们开发了一种独特的方法，使用机器学习算法来帮助再制造笔记本电脑定价。我们的方法涉及多种技术，包括加性模型、CART 分析、随机森林和多项式回归。在估算再制造笔记本电脑价格时，我们会考虑折旧和折扣因素，以考虑笔记本电脑不同的使用年限和状况。最后，我们还将估计价格与传统价格进行比较。总之，我们利用数据驱动的决策，并开发了一种强大的方法来为再制造笔记本电脑定价，以延长其使用寿命。