  Service supply chain management is to prepare spare parts for failed products
under warranty. Their goal is to reach agreed service level at the minimum
cost. We convert this business problem into a preference based multi-objective
optimization problem, where two quality criteria must be simultaneously
optimized. One criterion is accuracy of demand forecast and the other is
service level. Here we propose a general framework supporting solving
preference-based multi-objective optimization problems (MOPs) by multi-gradient
descent algorithm (MGDA), which is well suited for training deep neural
network. The proposed framework treats agreed service level as a constrained
criterion that must be met and generate a Pareto-optimal solution with highest
forecasting accuracy. The neural networks used here are two Encoder-Decoder
LSTM modes: one is used for pre-training phase to learn distributed
representation of former generations' service parts consumption data, and the
other is used for supervised learning phase to generate forecast quantities of
current generations' service parts. Evaluated under the service parts
consumption data in Lenovo Group Ltd, the proposed method clearly outperform
baseline methods.
