论文标题
通过开发优化模型和时间序列增长学位单元的分析来安排播种时间
Scheduling Planting Time Through Developing an Optimization Model and Analysis of Time Series Growing Degree Units
论文作者
论文摘要
在缩短的繁殖周期内生产高质量的农作物可确保全球粮食的可用性和安全性，但是由于存储限制，这种改善在全年繁殖过程中对种子工业的后勤和生产力挑战加剧了。在2021年分析中的先正达农作物挑战中，先正达提出了问题，以设计2020年全年繁殖过程中种植时间计划的优化模型，以便每周都有一致的收获数量。他们释放了一个数据集，其中包含2569种种子种群的种植窗，需要增长的学位单位进行收获，并在两个地点进行收获数量。为了应对这一挑战，我们开发了一个新框架，该框架由天气时间序列模型和一个优化模型组成，以安排种植时间。设计了一个深层复发的神经网络，以预测未来的天气，并且开发了时间序列模型的高斯过程模型，以模拟预测天气的不确定性。拟议的优化模型还安排了种子种群在最少的几周中的种植时间，每周收获数量更加一致。与原始的种植时间相比，使用提出的优化模型可以在场地0下将所需的容量降低69％，在现场1下降到51％。
Producing higher-quality crops within shortened breeding cycles ensures global food availability and security, but this improvement intensifies logistical and productivity challenges for seed industries in the year-round breeding process due to the storage limitations. In the 2021 Syngenta crop challenge in analytics, Syngenta raised the problem to design an optimization model for the planting time scheduling in the 2020 year-round breeding process so that there is a consistent harvest quantity each week. They released a dataset that contained 2569 seed populations with their planting windows, required growing degree units for harvesting, and their harvest quantities at two sites. To address this challenge, we developed a new framework that consists of a weather time series model and an optimization model to schedule the planting time. A deep recurrent neural network was designed to predict the weather into the future, and a Gaussian process model on top of the time-series model was developed to model the uncertainty of forecasted weather. The proposed optimization models also scheduled the seed population's planting time at the fewest number of weeks with a more consistent weekly harvest quantity. Using the proposed optimization models can decrease the required capacity by 69% at site 0 and up to 51% at site 1 compared to the original planting time.
