Instructor information
Module leader: Prof. Laura Palagi
Course information
ECTS: 6 credits
Status: Elective
Semester: 2
Hours: 30/18 (lectures/exercises)
Objectives
- Model real-world transport problems (traffic flow, routing, scheduling) using mathematical frameworks
- Apply optimization techniques to minimize cost, time, congestion, or emissions
- Develop decision-making skills under uncertainty (e.g., demand fluctuations, delays)
- Use data and algorithms to improve system performance (smart mobility, ITS)
- Understand trade-offs between efficiency, sustainability, and equity
Syllabus outline
- Description of optimization problems:
Decision types, objectives (cost, time, efficiency), role in transport systems. - Mathematical models:
Linear and integer programming; basic formulation and constraints. - Network analysis:
Nodes and links; shortest path and flow problems. - Decision under uncertainty:
Demand variability; basic probabilistic and simulation approaches. - Routing and logistics:
Vehicle routing, scheduling, and delivery optimization. - Public transport optimization:
Timetables, frequency, and resource allocation. - Advanced methods:
ITS, data-driven approaches, and performance evaluation.