Quantitative Methods for Logistics

The course contains of two parts; roughly 7 weeks each.

In the first part of the course, an introduction to basic operations research techniques will be presented:

- linear programming (simplex, duality etc.)

- integer programming (IP, BIP, MILP etc.)

- basic probability and statistics; brief overview of models to deal with uncertainty

This part of the course will largely be based on the first chapters of the book by Hillier and Liebermann.

During the first quarter, students must complete an individual practical assignment where they develop a mathematical model and implement it using Python together with Gurobi as a solver.

In the second part of the course, several common quantitative methods for transportation and logistics are presented.

- networks and routing: network flow problems, facility location problems, transportation problem, transshipment problem, vehicle routing problems and other standard problems; standard algorithms i.e. the branch and bound method.

- queueing theory: an overview of basic models and results.

During the second quarter, students must complete a practical assignment in groups on a case study with a transportation network again using Python.