URL study guide
https://tue.osiris-student.nl/onderwijscatalogus/extern/cursus?cursuscode=1CK130&collegejaar=2025&taal=enOmschrijving
Logistics networks are complicated entities that lie at the heart of companies’ abilities to competitively serve their customers. Examples include the infrastructure of a bike sharing system (drop-off and pick-up points, depots, bikes), the spare parts/service network of ASML (warehouses, parts, tools), full truck-load logistics service provider (trucks, trailers, consolidation points), etc.
Managing such logistics networks well (or not) can differentiate a well-running company from one battling to survive. Taking optimal operational decisions is next-to-impossible, since decisions must be taken fast (sometimes within split seconds) and are often subject to uncertainty and frequent input changes. Nonetheless, companies must gain insight into whether the decisions they take enable them to achieve the goals they set, and in this course you will learn to use simulations and digital twins for that purpose.
In this course, you will receive a working prototype (in Python) of a digital twin of a logistics network. This allows to directly visualize, measure, and analyze the consequences of operational decisions (e.g., inventory (re-) allocation, routing). Using real-world data, you will extend this digital twin with more and more functionality and details (including the decision-making of actors, forecasting, and operational algorithms). While extending the functionality, you will learn valuable skills on modelling complex business processes, that lie at the heart of many companies’ efforts to visualize, understand and improve their logistics networks.
For the given logistics network, we
- Simulate the basic functionality of the entities
- Analyze and prepare real-world data
- Develop simulations and digital twins to evaluate the performance of the network and the quality of operational decision rules
- Design and implement algorithm(s) to take / improve operational decisions
- Devise forecasting algorithms which improve the operational algorithms, and thus decisions
Doelstellingen
After this course, students are able to:
- To determine which techniques can be used to analyze and optimize the operations of logistics networks based on digital twins and simulation (LO1).
- To develop and implement digital-twin-like simulations of the logistics network of a given company and to apply the learned techniques to that simulation (LO2).
- To interpret the results obtained from analysis and optimization of such digital twins, and propose improvements to day-to-day operations of logistics networks based on that analysis (LO3).