Studying at the University of Verona
Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.
Study Plan
The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University.
Please select your Study Plan based on your enrollment year.
1° Year
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2° Year It will be activated in the A.Y. 2026/2027
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One module to be chosen among the following| Modules | Credits | TAF | SSD |
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One module to be chosen among the following| Modules | Credits | TAF | SSD |
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Legend | Type of training activity (TTA)
TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.
Logistic optimization (2025/2026)
Teaching code
4S012439
Academic staff
Coordinator
Credits
9
Language
Italian
Scientific Disciplinary Sector (SSD)
MAT/09 - OPERATIONS RESEARCH
Period
Secondo semestre LM dal Feb 16, 2026 al May 20, 2026.
Courses Single
Authorized
Learning objectives
Aim of this course is to lead students to be able to understand the basic notions and tools of Operations Research in support of the strategic and operational planning of companies, with a particular emphasis to the optimisation of cost and margins regarding the logistics of transport of goods.
At the end of the course, students will have to show the knowledge and the ability to understand the main optimisation methods of Operations Research. They will also have to show the ability to understand, analyse and implement, also by means of specific software, the optimisation models suitable for solving relevant decision-making problems in the field of the logistics of transport of goods.
Prerequisites and basic notions
Basic knowledge of analysis (numbers, sets, functions), algebra and calculus (equations and unknowns, solution of systems of linear equations), analytical geometry (Cartesian coordinates, straight line and plane equations), linear algebra (vectors and matrices) calculus differential and integral.
Program
The course program will focus on optimization techniques for logistics and supply chain management problems. Several practical applications will be addressed in the form of case studies. In particular, we will initially focus on the basics of optimization and mathematical modeling of a logistics problem, providing various examples; we will then address more specific problems such as: node localization, warehouse management, transportation, vehicle routing problems and supplier management. The various problems will be analyzed through the guided use of dedicated software both for the aspects of mathematical optimization and data analysis. The details of the course contents are as follows:
*Introduction to logistics optimization: Overview of problems in logistics and supply chain management; introduction to optimization techniques; applications of logistics optimization.
*Linear programming and optimization paradigms: basic notions of Linear Programming, outline of solution methods for LP (simplex).
*Mixed Integer Linear Programming, modeling techniques, mathematical formulation of constraint structures and notable problems.
* Nonlinear optimization, KKT conditions, Lagrangian relaxation, and numerical methods: gradient method, interior point method.
* Logistics node location: Qualitative and quantitative location methods, continuous and discrete location problems, multi-product and coverage problems.
* Warehouse management: Performance metrics and decision problems, warehouse design and equipment selection, storage and retrieval strategies.
* Transportation management: Transportation modes and transportation problem classification, minimum-cost flow problems, traffic assignment and network design.
* Vehicle routing problems (VRP): Traveling salesman problem (TSP) and VRP variants, capacity and time constraints in vehicle routing, real-time vehicle routing problems.
* Integrated optimization problems: Integrated location and routing problems, inventory-routing problems.
* Supplier management: Supplier search and selection criteria, supplier evaluation and decision making, supplier evaluation and decision making.
*Forecasting and data analysis in logistics: Qualitative and quantitative methods for data analysis, time series analysis and forecasting.
*Software tools for logistics optimization: Introduction and use of software dedicated to logistics optimization (eg, Python/MATLAB programming language).
Optional topics:
- Stochastic models in logistics (stochastic optimization techniques, queueing theory and Markov decision processes). - Heuristic and meta-heuristic methods, genetic algorithms, simulated annealing and applications.
- Multi-Criteria and Multiobjective Optimization.
Bibliography
Didactic methods
Lectures with slides, teacher's notes and hand-on sessions in the classroom.
Learning assessment procedures
Written and oral with the possibility of supplementing the examination with a project and exercises during the course.
To pass the exam, students must demonstrate that:
- They have understood the principles underlying optimization techniques applied to logistics problems.
- They are able to present arguments on the topics of the course in a precise and organic way.
- They know how to apply the knowledge acquired to solve application problems presented in the form of exercises, questions and projects.
Evaluation criteria
Discussed and agreed with the aim that they can be both fair and reasonable.
Criteria for the composition of the final grade
Average of written and oral mark, and possible integration of the grade with the carrying out of projects and exercises.
Exam language
Italiano
