Data-driven Optimization using Digital Twins for Sustainable Last-Mile Delivery
Due to the complexity of modern supply chains, it is difficult to predict what the effect will be of a decision aimed at reducing greenhouse gas emissions, such as choosing the location of a pick-up point, or changing the travel route for a vehicle. Digital twins make it possible to try these decisions in a virtual environment before applying them in real life. This helps policymakers in governments and companies gain a better understanding of the consequences of a decision, which reduces the risks and uncertainties of the radical new decisions that are necessary to achieve the sustainable supply chain of the future. With the rise of digital twins for smart cities, such as the Atlas Livable City developed by the Logistics Community Brabant, more data is readily available than ever before. Yet most existing optimization techniques, which are necessary for minimizing an objective such as travel time or greenhouse gas emissions, are not able to deal with such complex virtual environments. Data-driven optimization techniques are therefore an active area of research. Examples of this are optimization heuristics learned with machine learning, and surrogate models for optimization. This project will contribute to this active research landscape by making data-driven optimization techniques that are suitable for digital twins. The main application is the reduction of greenhouse gas emissions in last-mile delivery by choosing the locations of pick-up points in urban environments.