This thesis investigates the use of neural networks for forecasting order volumes and introduces a framework that integrates these forecasts into route planning for B2B wholesale distributors. The proposed routing strategy features a two-stage optimization process: first, provisional routes are created based on predicted orders. Then, after actual orders are revealed, final routes are generated, taking the provisional plan into account.

A key operational challenge arises when the provisional and final routes differ. Customer orders assigned and sorted for delivery may need to be relocated, leading to increased manual handling. Since actual relocations are difficult to measure in simulations, this study defines a “swap” as the event where a customer is scheduled on a different route than initially planned. The main objective is to minimize these swaps while ensuring feasible and efficient delivery routes. This is achieved by improving forecasting accuracy and designing a framework to integrate forecasts into the routing process.

The research leverages ORTEC’s B2B Delivery software for route optimization, which allows preplanned routes as input but does not explicitly consider uncertainty. Various optimizer configurations and strategies are tested to assess their impact on swap reduction and route efficiency. For implementation, we propose a two-stage optimization strategy, which, due to limitations of the optimizer, includes two optimization rounds in the second stage. First, a full optimization is performed where customers from the provisional plan are fixed in their preplanned routes. This is followed by a light optimization round that encompasses all customers. In the light optimizer, raising the minimum estimated gain before attempting swaps had a more significant effect on reducing the number of swaps compared to increasing the number of swap attempts. Tests on forecasting qualities highlighted the importance of global metrics, such as the total number of customers and forecasted volume, on swap reduction and route efficiency.

Prior to this research, a proof of concept was conducted to forecast thousands of time series using traditional forecasting methods, which revealed two significant flaws. Firstly, the necessity to train separate forecasting models for individual time series resulted in the training of tens of thousands of models. Secondly, forecasting for new customers with limited historical data proved challenging. This motivated the adoption of neural network models as a unified solution, also capable of learning from similar patterns across multiple time series. This research tests three state-of-the-art neural network models: DeepAR from Amazon, Temporal Fusion Transformer (TFT) from Google, and N-HiTS from Nixtla. While all models struggled with the zero-inflated nature of order data, separating the prediction of order occurrence from order volume gave comparable or improved results to the traditional approach. The neural network models also show improvements in terms of forecasting for new customers. The main advantage lies in the ability to maintain just two models that function across all customers, eliminating the need for separate models for each customer.

DeepAR provided the most consistent results in route planning, despite a mean absolute error, likely due to superior global forecasting. TFT, while more accurate, led to more swaps due to systematic underestimation of the total volume. N-HiTS was the least effective but trained fastest.

Future research should explore improved loss functions of the neural network models and hierarchical forecasting. Incorporating stochastic optimization could further enhance the integration of forecasts into routing under uncertainty.