Large-scale optimization for transport & mobility

URL study guide

Description

The lectures cover an overview of (meta-)heuristic solution techniques for solving large-scale optimization problems arising in transport and mobility. The course discusses constructive heuristics, local search heuristics and mixed-integer programming based heuristics. Part of the lectures are also devoted to case studies, to see how these techniques can be adapted, combined and applied to rich large-scale problems faced in practice 
 
Additional information Tests: 
The assignments ask the students to implement and analyze the performance of the discussed methods, testing theknowledge, understanding, and implementation of (meta)heuristic solution techniques for large-scale optimization problems arising in transport and mobility.  
 
The individual assignment assignment provide step-by-step guidance on solving the respective problems. The group assignment allows more freedom, and asks the student  
to solve a rich and more practical large-scale optimization problem for which the state-of-the-art does not yet provide solutions.  
 
All assignments require a report to be handed in, and the solution of a given dataset to test the performance and efficiency of the approach. 

Objectives

The scale of transport and mobility networks is ever-increasing. This course focuses on exploring the area of optimization methods that are tailored for the large-scale optimization that arise in such applications, with a focus on (meta)-heuristics.  Upon successful completion of this course, students are able to 

• explain and differentiate between common classes of heuristic algorithms,  
• explain the strengths and weaknesses of state-of-the-art meta-heuristics, 
•  implement heuristic algorithms in code to solve practical problems in transport and mobility, 
•  analyze the performance of heuristic solution methods. 

Method of Assessment