An MILP Model for Corn Planting and Harvest Scheduling Considering Storage Capacity and Growing Degree Units

Corn planting and harvest scheduling is an important problem due to having a significant impact on corn yield, balancing the capacities for harvest, transport, and storage operations. Different corn hybrids also have different planting window and poor planting and harvest schedules may cause erratic weekly harvest quantities and logistical and productivity issues. In the 2021 Syngenta Crop Challenge, Syngenta released several large datasets that recorded the historical daily growing degree units (GDU) of two sites and provided planting window, required GDUs, and harvest quantity of corn hybrids planted in these two sites. Then, participants of this challenge were asked to schedule planting and harvesting dates of corn hybrids under two storage capacity scenarios so that facilities are not over capacity in harvesting weeks and have consistent weekly harvest quantities. The two storage capacity scenarios include: (1) planting and harvest scheduling given the maximum storage capacity, and (2) planting and harvest scheduling without maximum storage capacity to determine the lowest possible capacity for each site. In this paper, we propose two mixed integer linear programming (MILP) models for solving this problem considering both the storage capacity and the uncertainty in GDUs. Our results indicate that our proposed models can provide optimal planting and harvest scheduling under different GDU possibilities which ensures consistent weekly harvest quantities that are below the maximum capacity.