论文标题
半空还是半满？一种预测消费者回收行为以增加反向自动售货机正常运行时间的混合方法
Half-empty or half-full? A Hybrid Approach to Predict Recycling Behavior of Consumers to Increase Reverse Vending Machine Uptime
论文作者
论文摘要
反向自动售货机（RVM）是促进闭环塑料包装回收的经过验证的工具。 RVM的良好客户体验对于进一步扩散该技术至关重要。 bin完整事件是RVM市场世界领导者反向自动售货机（RVM）停机时间的主要原因。手头的论文开发并评估了一种基于机器学习和预见到完整事件的统计近似的方法，从而增加了RVM的正常运行时间。我们的方法依赖于预测给定RVM的返回饮料容器的小时时间序列。我们通过在零售环境中开发和评估每小时预测的方法来做出贡献 - 应用域和预测粒度的组合是新颖的。痕量驱动的模拟证实，基于预测的方法比幼稚的排空策略所导致的停机时间和成本更少。
Reverse Vending Machines (RVMs) are a proven instrument for facilitating closed-loop plastic packaging recycling. A good customer experience at the RVM is crucial for a further proliferation of this technology. Bin full events are the major reason for Reverse Vending Machine (RVM) downtime at the world leader in the RVM market. The paper at hand develops and evaluates an approach based on machine learning and statistical approximation to foresee bin full events and, thus increase uptime of RVMs. Our approach relies on forecasting the hourly time series of returned beverage containers at a given RVM. We contribute by developing and evaluating an approach for hourly forecasts in a retail setting - this combination of application domain and forecast granularity is novel. A trace-driven simulation confirms that the forecasting-based approach leads to less downtime and costs than naive emptying strategies.
