LOG904-190 Mixed Integer Programming Models for Logistics Optimization (Autumn 2026)

Mixed integer programming (MIP) approaches are widely used to effectively solve complex problems in various areas of operations and logistics optimization. This seminar provides an introductory and practical exploration of MIP and Integer Programming (IP) methods and their applications within different logistics and operations contexts. The course begins with basic definitions and a brief overview of key practical applications, introducing the fundamental ideas behind some of the most commonly used solution algorithms. Emphasis is placed on the modelling and implementation of MIP/IP formulations using a programming language in together with an MIP optimization solver. Students will learn how to model several objectives and constraints typically arising in logistics and industrial processes, including assignment, production and procurement planning, transportation, and scheduling. The course adopts a structured modelling approach:

• Problem description from practical logistics processes.
• Mathematical formulation as a MIP/IP model.
• Implementation in the programming environment.

This hands-on course is designed for logistics students seeking to acquire practical modelling skills applicable to real-world operations and logistics challenges.

• Basic mathematical knowledge, especially algebra and logical reasoning.
• Introductory programming experience (e.g., using loops, functions) in any language.
• Familiarity with linear programming is advantageous but not essencial.
• Familiarity with basic logistics and operations concepts is advantageous but not essential.

The student’s learning outcomes after completing the course:

Knowledge:

• Understand the principles and scope of MIP/IP methods in logistics optimization.
• Recognize common logistics optimization problems and their mathematical structures.
• Gain insight into modelling practical logistics processes.

Skills:

• Formulate real-world operations and logistics problems as MIP/IP models.
• Implement and solve optimization models using a programming language and an optimization solver.
• Analyze and interpret computational results to support logistics decision-making.

General competence:

• Apply MIP/IP modelling techniques effectively to logistics and operations problems.
• Communicate modelling choices and optimization results clearly.
• Reflect on the practical use and limitations of optimization in logistics contexts.

• Five lectures along one week, one lecture per day, three hours per lecture (with two breaks).
• Two practical sessions to implement formulations, three hours per session (with two breaks).
• Hands-on modelling and implementation exercises.
• Case studies and logistics applications.

Examination:

• Form of assessment: Formulate classical problems as MIP/IP models, implement them using a programming language and a MIP solver, solve them, and submit the implementations.
• Proportion: 30 %
• Duration: submission at the end of Day 2.
• Grouping: Groups of two students.
• Grading scale: Letter (A - F)
• Support material: All materials permitted, including programming libraries, textbooks, notes, and online resources.

• Form of assessment: Select or be assigned a practical logistics problem, formulate it as a MIP/IP model, implement it using a programming language and a MIP solver, solve it, and submit the implementations and a concise written report detailing their modelling process and results.
• Proportion: 70 %
• Duration: submission at the end of Day 5.
• Grouping: Groups of two students.
• Grading scale: Letter (A - F)
• Support material: All materials permitted, including programming libraries, textbooks, notes, and online resources.