A neural network model for solving the lot-sizing problem

Artificial neural network models have been used successfully to solve demand forecasting and production scheduling problems; the two steps that typically precede and succeed Material Requirements Planning (MRP). In this paper, a neural network model is applied to the MRP problem of lot-sizing. The model's performance is evaluated under different scenarios and is compared to common heuristics that address the same problem. Results show that the developed artificial neural network model is capable of solving the lot-sizing problem with notable consistency and reasonable accuracy.