Visit to RMIT, Melbourne, Australia

Helena taught a course on Metaheuristics for Combinatorial Optimization at the RMIT, Melbourne, Australia.

Erasmus+The course was financially supported by the ERASMUS+ International Program. Helena would like to thank the program and  all the team at the “Servei de Relacions Internacionals” of UPF that have helped in making this course possible.

RMIT2019The course will focus on Metaheuristics applied to Combinatorial Optimization to solve large-scale business problems based on the scientific method. Combinatorial Optimization problems like Routing, Location, and Scheduling problems will be described. Heuristics and metaheuristics algorithms, two relevant methodologies to solve these problems, are presented and explained in detail. It will be also presented some real applications of these problems in different areas as Operations Management, Healthcare, Marketing, Logistics, Supply Chain Management, etc. Finally, it will be discussed several real applications of metaheuristics to solve combinatorial optimization in different businesses as for example Seat, Inditex (Zara), CatLab, Area de Guissona, etc.

Metaheuristics are particularly attractive in the efficient and effective solution approach of combinatorial optimization problems. Metaheuristics are general high-level procedures that coordinate heuristics and rules to find high-quality solutions to difficult optimization problems. They are based on distinct paradigms and offer different mechanisms to go beyond the first solution obtained that cannot be improved by local search. They are frequently built upon a number of common building blocks such as greedy algorithms, randomization, neighborhoods and local search, etc. They are design to solve large-scale complex optimization problems that cannot be solved in reasonable processing time by the classic combinatorial optimization methods.

The first goal of this course is to give the students a general idea of the class of problems that benefit from and are amenable to be efficiently solvable by metaheuristics. With this goal in view, the course starts by a gentle introduction to Business Analytics and Combinatorial Optimization. The second goal of the course is to present to the students the main metaheuristics and their building blocks, so as that they could be able to propose or even develop simple solution strategies for practical problems.


  1. A gentle introduction to Operations Research & Business Analytics.
  2. Combinatorial Optimization Problems: Location, Routing and Scheduling.
  3. Metaheuristics Algorithms.
  4. Iterated Local Search.
  5. Briefly description of the extensions of Iterated Local Search (ILS): SimILS and MathILS.
  6. Application of Metaheuristics to problems arising in Supply Chain Management area. Description of real applications.