I. Course Overview
This course introduces advanced optimization tools and techniques with the main emphasis being on the application of computational intelligence algorithms to different large scale problems and cases that arise in business and industry, such as transportation, logistics, production and services.
II. Learning Outcomes
On completion of this course, students should be able to: broaden their exposure to computational methodologies; analyze and design effective computational intelligence algorithms for large scale business problems, and; provide examples and cases of how the computational intelligence algorithms can be used to solve real-life problems. The course material includes the following thematic areas: greedy and local search algorithms; variable neighbourhood search (VNS); tabu search algorithms; ant colony optimization; evolutionary algorithms.
III. Teaching Methods
The course is taught in lecture format, and illustrates key concepts by using case-based teaching, examples, team working class-exercises, an individual assignment and Lab-based tutorials.
IV. Course Material
“Large Scale Optimization”, Teaching Notes, by C.D. Tarantilis, 2015
Recommended textbook for the course is:
Gendreau M, Potvin J-Y (Eds.). Handbook of Metaheuristics, International Series in Operations Research & Management Science, Vol. 146, 2nd ed, Springer, 2010
Talbi E-G. Metaheuristics: From Design to Implementation. Wiley, 2009
V. Course Evaluation
Final Exam: 80%.
Individual Assignment: 20%
- Διδάσκων: Christos Tarantilis
- Διδάσκων: ΕΜΜΑΝΟΥΗΛ ΖΑΧΑΡΙΑΔΗΣ
- Διδάσκων: ΕΛΕΥΘΕΡΙΟΣ ΜΑΝΟΥΣΑΚΗΣ