  An algebraic modeling system (AMS) is a type of mathematical software for
optimization problems, which allows users to define symbolic mathematical
models in a specific language, instantiate them with given source of data, and
solve them with the aid of external solver engines. With the bursting scale of
business models and increasing need for timeliness, traditional AMSs are not
sufficient to meet the following industry needs: 1) million-variable models
need to be instantiated from raw data very efficiently; 2) Strictly feasible
solution of million-variable models need to be delivered in a rapid manner to
make up-to-date decisions against highly dynamic environments. Grassland is a
rapid AMS that provides an end-to-end solution to tackle these emerged new
challenges. It integrates a parallelized instantiation scheme for large-scale
linear constraints, and a sequential decomposition method that accelerates
model solving exponentially with an acceptable loss of optimality. Extensive
benchmarks on both classical models and real enterprise scenario demonstrate 6
~ 10x speedup of Grassland over state-of-the-art solutions on model
instantiation. Our proposed system has been deployed in the large-scale real
production planning scenario of Huawei. With the aid of our decomposition
method, Grassland successfully accelerated Huawei's million-variable production
planning simulation pipeline from hours to 3 ~ 5 minutes, supporting
near-real-time production plan decision making against highly dynamic
supply-demand environment.
