Metaheuristics Course
Metaheuristics Course
Date
21 – 23 February 2018
Total: 18 hours
Location
Auditori de Mercé Rodoreda,
Campus Ciutadella, Universitat Pompeu Fabra
Potential participants
Master and PhD students, postdoc researchers and any researcher with interest in the topic.
Metaheuristics Graduate course syllabus
Learning objectives – Overview and goals
Metaheuristics are general high-level procedures that coordinate simple 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, reduced neighborhoods and candidate lists, intensification, diversification, path-relinking, and periodical restarts. Metaheuristics are among the most effective solution strategies for solving combinatorial optimization problems in practice and very frequently produce much better solutions than those obtained by the simple heuristics and rules they coordinate. They are designed to solve large-scale optimization problems that cannot be solved in reasonable processing time by the classic combinatorial optimization methods.
Metaheuristics are particularly attractive in the efficient and effective solution of logistic decision problems in supply chains, transportation, telecommunications, vehicle routing and scheduling, manufacturing and production, timetabling, sports scheduling, facility location and layout, network design, and power generation, finance, marketing, among other areas.
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 and intuitive introduction to complexity theory. 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. The students will learn the main concepts relevant for the design and application of metaheuristics. Finally, the third goal of the course consists in showing and discussing with the students several practical applications of metaheuristics to real problems in logistics, retailing, marketing, sports, finance etc. in different industries as for example Amazon, Seat, Inditex, Federal Express.
Streaming will be available here.
REGISTRATION IS NOW CLOSED. If you wish to attend, please contact Helena Ramalhinho.
Date
21 – 23 February 2018
Total: 18 hours
Location
Auditori de Mercé Rodoreda,
Campus Ciutadella, Universitat Pompeu Fabra
Potential participants
Master and PhD students, postdoc researchers and any researcher with interest in the topic.
Metaheuristics Graduate course syllabus
Learning objectives – Overview and goals
Metaheuristics are general high-level procedures that coordinate simple 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, reduced neighborhoods and candidate lists, intensification, diversification, path-relinking, and periodical restarts. Metaheuristics are among the most effective solution strategies for solving combinatorial optimization problems in practice and very frequently produce much better solutions than those obtained by the simple heuristics and rules they coordinate. They are designed to solve large-scale optimization problems that cannot be solved in reasonable processing time by the classic combinatorial optimization methods.
Metaheuristics are particularly attractive in the efficient and effective solution of logistic decision problems in supply chains, transportation, telecommunications, vehicle routing and scheduling, manufacturing and production, timetabling, sports scheduling, facility location and layout, network design, and power generation, finance, marketing, among other areas.
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 and intuitive introduction to complexity theory. 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. The students will learn the main concepts relevant for the design and application of metaheuristics. Finally, the third goal of the course consists in showing and discussing with the students several practical applications of metaheuristics to real problems in logistics, retailing, marketing, sports, finance etc. in different industries as for example Amazon, Seat, Inditex, Federal Express.
Streaming will be available here.
REGISTRATION IS NOW CLOSED. If you wish to attend, please contact Helena Ramalhinho.
Lecturers
Christian Blum
Artificial Intelligence Research Institute, IIIA-CSIC
Angel A. Juan
Internet Computing & Systems Optimization research group – IN3
Universitat Oberta de Catalunya
Jésica de Armas
Universitat Pompeu Fabra & BGSMath
Belén Melián-Batista
Universidad de La Laguna
Sofiane Oussedik
ILOG optimization technical sales manager at IBM
Helena Ramalhinho
Director of the Business Analytics Research Group
Universitat Pompeu Fabra & BGSMath
Celso Ribeiro
Universidade Federal Fluminense | Instituto de Ciência da Computação
Organisers
Contents
- Introduction to Combinatorial Optimization and Applications
- A gentle introduction to the analysis of algorithms and complexity theory
- Greedy algorithms and Local search
- Building blocks: randomization, intensification, path-relinking, diversification, restarts
- Simulated annealing
- Tabu search
- Greedy randomized adaptive search procedures (GRASP)
- Variable neighborhood search (VNS)
- Genetic algorithms
- Iterated Local Search and Applications
Full schedule
Wednesday, 21 February
11.30 – 13.30
Helena Ramalhinho: Introduction to Combinatorial Optimization and Applications
Lunch
15.00 – 17.00
Sofiane Oussedik: Introduction to CPLEX
Thursday, 22 February
9.00 – 11.00
Helena Ramalhinho: Iterated Local Search
Coffee Break
11.30 – 12.30
Belén Melián-Batista: Application of Metaheuristics in Port Logistics
12.30 – 13.30
Angel Juan: Simheuristics: extending metaheuristics to cope with real-life uncertainty
Lunch
15.00 – 17.00
Christian Blum: Hybrid metaheuristics: Combining metaheuristics with other techniques for optimization
17.00 – 18.30
Jésica de Armas: Machine Learning & Metaheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs
Workshop Dinner
Friday 23 February
9.00 – 11.00
Fatos Xhafa: Meta-heuristics for Cloud Optimisation
Coffee Break
11.30 -12.30
Angel Juan: Biased Randomized Algorithms with Applications
13.00 – 13.30
Closing remarks
Short talks
- Simheuristics: extending metaheuristics to cope with real-life uncertainty (Angel A. Juan)
- Hybrid metaheuristics: combining metaheuristics with other techniques for optimization (Christian Blum)
- Machine Learning & Metaheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs (Jésica de Armas)
- Application of Metaheuristics in Port Logistics (Belén Melián-Batista)
- CPLEX (IBM to be confirmed!)
Minimum requirements and assessment criteria
The students should have basic knowledge of Algebra, Programming in any language and Operations Research.
At the end of this course, students will know what metaheuristics are, why they are needed, how to design them, and how to evaluate their quality. The students will learn the main concepts relevant for the design and application of metaheuristics. The students will also be familiar with some of their real applications of metaheuristics in companies in different industries.
References
- M. G. C. Resende and Celso C. Ribeiro (2016), Optimization by GRASP: Greedy Randomized Adaptive Search Procedures, Springer.
- M. Gendreau and J.-Y. Potvin (2010), editors, Handbook of Metaheuristics, 2nd edition, Springer..
- E. K. Burke and G. Kendall (2014), editors, Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques, 2nd edition, Springer.
- H. H. Hoos and T. Stützle (2005), Stochastic Local Search: Foundations and Applications, Elsevier.
- C. Blum and G.R. Raidl (2016), Hybrid Metaheuristics — Powerful Tools for Optimization. Springer.
Opportunities
Metaheuristics is becoming a reference approach to solve real large-scale optimization problems. Companies like Amazon, Inditex, Seat, etc. are applying metaheuristics to solve their optimization problems in Logistics, Production, e-commerce for example. Celso Ribeiro, Helena Ramalhinho, Christian Blum, Jésica de Armas and Belén Melián are leader researchers in the area of Metaheuristics and their application to real life problems.