Course detail
Computing Methods in Logistics Optimization Problems
FSI-SOU-A Acad. year: 2024/2025 Summer semester
The course introduces the students to the software tools used for modeling and solving different types of optimization problems. The main content of the course lies in recognizing and using suitable models and methods for specific logistics problems.
Language of instruction
English
Number of ECTS credits
5
Supervisor
Department
Entry knowledge
The presented topics require basic knowledge of concepts from optimization, statistics, and programming.
Rules for evaluation and completion of the course
Course-unit credit requirements: active participation in seminars, mastering the subject matter, and semester project acceptance.
Examination: Written exam focused on the successful implementation of the discussed models and methods accompanied by oral discussion of the results.
Attendance at seminars is required as well as active participation. Passive or missing students are required to work out additional assignments.
Aims
The emphasis is on the acquisition of application-oriented knowledge of logistics optimization models and methods, and on the use of computers and available software tools.
The student will acquire the ability to recognize a suitable optimization model for a given logistics problem. The student will be able to implement the said model in an adequately chosen software tool and perform a thorough analysis of the results.
The study programmes with the given course
Programme N-LAN-A: Logistics Analytics, Master's, compulsory
Programme C-AKR-P: , Lifelong learning
specialization CLS: , elective
Type of course unit
Lecture
13 hours, optionally
Syllabus
1. Software tools for optimization, languages/environments (EXCEL, MATLAB, Julia). The use of solvers.
2. Implementation of basic optimization model types (linear, quadratic, integer, etc.).
3. Network-based optimization models.
4. Shift scheduling and Staff planning models.
5. Location-allocation and Facility location models.
6. Knapsack, Capital budgeting, and Bin packing models.
7. Travelling salesman problem, lazy constraints.
8. Multi-objective optimization and multi-criteria decision analysis methods.
9. Optimization in simulation environments, black-box optimization.
10. Surrogates for computationally expensive problems, selection and validation.
11. Surrogate-assisted optimization.
12. Stochastic programming, generation of scenario trees.
13. Geographic information system (GIS) software.
Exercise
26 hours, compulsory
Syllabus
The exercise follows the topics discussed in the lecture. The main focus is on software implementation.