THESIS
2018
xi, 75 pages : illustrations ; 30 cm
Abstract
This thesis focuses on the two fundamental issues in manufacturing. One is to forecast
the demand quantity of products in the coming time periods. After the demand forecasting,
the next key important step is to schedule the corresponding production planning.
In the first part, we consider a long-term demand forecasting problem. Due to the
fact that the sample data may be very limited and of high variation, traditional prediction
models can not perform well here. Inspired by the ensemble idea in Random Forest, we
apply this ensemble idea together with the random subset sampling on the training data
and propose a new forecasting model named Data-ensemble Random Forest (DERF). We
prove that this new model retains the convergence property of Random Forest, i.e., as the
number of t...[
Read more ]
This thesis focuses on the two fundamental issues in manufacturing. One is to forecast
the demand quantity of products in the coming time periods. After the demand forecasting,
the next key important step is to schedule the corresponding production planning.
In the first part, we consider a long-term demand forecasting problem. Due to the
fact that the sample data may be very limited and of high variation, traditional prediction
models can not perform well here. Inspired by the ensemble idea in Random Forest, we
apply this ensemble idea together with the random subset sampling on the training data
and propose a new forecasting model named Data-ensemble Random Forest (DERF). We
prove that this new model retains the convergence property of Random Forest, i.e., as the
number of training rounds increases, DERF will alway converge. Although DERF is based on Random Forest, its construction idea could also be extended to any other model. By
numerical experiment, we show that DERF achieves a significant improvement in performance
compared with the traditional prediction models, such as ARIMA, Random Forest,
Gradient Boosting Decision Tree, and Long Term Short Memory.
In the second part, in order to solve a large scale production scheduling integer programming,
whose objective is to minimize the total holding cost, within a reasonable time,
we decompose this problem into four steps. We first regard all different plants as a whole
one, and obtain an upper bound of the amount of each product that should be produced.
Then, we assign the above production tasks for each plant based on its own constraints.
Afterwards, other decision variables such as the supply amount of each product, and the
transfer amount of each product between plants will be updated accordingly. Numerically,
the gap between the optimal solution of the original problem and the one obtained
by our decomposition algorithm is quite small.
Post a Comment