Sustainable Business Development

Das neue, europaweit einzigartige Master-Studium: trinational, interdisziplinär und praxisorientiert.
Bewerben
M+V Department

Sustainability 3: Nachhaltigkeit und Künstliche Intelligenz

Empfohlene Vorkenntnisse
  • Digital skills (willingness to work with software)
  • Willingness to write a scientific paper
Lehrform Vorlesung
Lernziele / Kompetenzen

The objectives of the module are to enable students to solve complex real-world optimisation problems numerically, using special methods belonging to Artificial Intelligence (AI) - in particular methods inspired by nature, called Computational Intelligence (CI). A given numerical approach is used to solve and optimise these complex problems focusing on a particular sustainability aspect in business (e.g. logistics, finance), engineering (e.g. transport, energy consumption, renewable energy) or natural/environmental sciences (e.g. climate change).Students learn about AI with a focus on CI and how to model, numerically simulate and optimise real-world problems on the computer. The following AI topics are emphasized: Evolutionary Computation, Swarm Intelligence, Neural Networks, Fuzzy Logic, Metaheuristics, Robotics, and future de-velopments and ethics of AI.Frontal teaching is reduced to short introductions to the topics and the module project. Most of the learning time in this module is spent by the students on their own research project, including modelling an application problem, developing (i.e. coding, implementing, customising) a software prototype (artefact) based on design science research.In general, the guiding principle of this module is as follows: (1) take a real-world problem focusing on a particular sustainability aspect, (2) de-rive a simplified computational problem model (following the proposed optimisation problem approach), (3) apply a particular optimisation meth-od (belonging to AI), (4) compute optimised solutions, and (5) discuss the results.
Dauer 1
SWS 2.0
Aufwand
Lehrveranstaltung 30
Selbststudium / Gruppenarbeit: 60
Workload 90
ECTS 3.0
Voraussetzungen für die Vergabe von LP

Hausarbeit
Modulverantwortlicher

Rolf Dornberger (FHNW)
Empf. Semester 3
Haeufigkeit jedes Jahr (WS)
Verwendbarkeit

Masterstudiengang SBD
Veranstaltungen

KI-basierte Optimierung von Nachhaltigkeitsaspekten

Art Vorlesung
Nr.
SWS 2.0
Lerninhalt
  • Areas of application focusing on a particular aspect of sustainability where AI can provide support and potential impact: logistics, energy, business, engineering, finance, economics, management, computer science, robotics, etc.
    provide support and potential impact: logistics, energy, business, engineering, finance, economics, management, computer science, robotics, etc.
  • Fundamentals of modelling, simulation and optimisation.
  • Numerical optimisation problems consisting of problem models and optimisation methods: Definition, evaluation and solution of optimisation problems (objectives, constraints, parameter sets, etc.).
  • Foundations of computational intelligence, nature-inspired AI, specific AI methods and meta-heuristics: e.g. evolutionary computation (e.g. genetic algorithm, evolutionary strategy), swarm intelligence, neural networks, fuzzy logic, meta-heuristics, open systems, computational creativity, etc.
  • Computational Science: Use of software engineering and programming to model, simulate and optimise problems, using software and optimisation platforms.
Literatur

wird in der Vorlesung bekannt gegeben