Abstract.

The agro-food industry wastes tons of oil and grease not suitable for immediate consumption. Their collection mostly relies on the experience of managers and this results in inaccurate visits by truck drivers and operations teams. Indeed, the measurement of by-products waste is complex and thus information is imprecise, making the collecting operations inefficient. In this paper, we propose a model that forecasts the daily input of thousands of industrial and commercial sites of the agro-food industry based on historical data. The algorithm rejects errors and mistakes in the routing-collection-measuring process. In our model, the site container capacity is known and remains constant. The main contribution of this study is to propose a model based on the Theil-Sen constrained regression (Theil-Sen CR) that rejects errors and outliers to simplify the forecast of future collections. We apply this method to a real case study and compare its performance at different collecting sites. The forecasting error is significant compared to Linear Regression (LR). We have calculated, for our industrial partner, based on 12.2 km between sites and a fleet of 200 trucks, a potential reduction of 940 tCO₂ equivalent per year.

摘要。

农业食品工业浪费了不适合立即消费的大量油脂。他们的收藏主要取决于管理人员的经验，这导致卡车司机和运营团队的访问不准确。实际上，副产品废物的测量很复杂，因此信息不准确，从而使收集操作效率低下。在本文中，我们提出了一个模型，该模型根据历史数据来预测农业食品行业的数千个工业和商业场所的日投入量。该算法拒绝路由收集测量过程中的错误和错误。在我们的模型中，站点容器容量是已知的并且保持不变。这项研究的主要贡献是提出了一个基于Theil-Sen约束回归（Theil-Sen CR）的模型，该模型可以拒绝误差和离群值以简化对未来馆藏的预测。我们将此方法应用于实际案例研究，并比较其在不同收集地点的性能。与线性回归（LR）相比，预测误差显着。对于我们的工业合作伙伴，我们根据站点之间的12.2公里和200辆卡车的车队计算出，可能会减少940吨二氧化碳当量每年_2个同等学历。