Verolog

MESS 2020+1 - Metaheuristics Summer School
2021-06-15 - 2021-06-18
Call for Participation
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– MESS 2020+1 – Metaheuristics Summer School
– Learning & Optimization from Big Data –
15-18 June 2021, Catania, Italy
(virtual and onsite mode)
https://www.ANTs-lab.it/mess2020/
mess.school@ANTs-lab.it
https://www.facebook.com/groups/MetaheuristicsSchool/
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** APPLICATION DEADLINE: 5th March 2021 **
https://www.ants-lab.it/mess2020/application/
MESS 2020+1 is aimed at qualified and strongly motivated MSc and PhD students; post-docs; young researchers, and both academic and industrial professionals to provide an overview on the several metaheuristics techniques, and an in-depth analysis of the state-of-the-art. The main theme of this edition is “Learning and Optimization from Big Data”, therefore MESS 2020+1 wants to focus on (i) Learning for Metaheuristics; (ii) Optimization in Machine Learning; and (iii) how Optimization and Learning affect the Metaheuristics making them relevant in handling Big Data.
Participants will be delivered a certificate of attendance indicating the number of hours of lectures (36-40 hours of lectures). In according to the academic system all PhD and master students attending to the summer school will may get 8 ECTS points.
** LIST OF LECTURERS & LECTURES TITLES
+ Paolo Arena, Unviersity of Catania, Italy
Lecture#1: TBA
Lecture#2: TBA
+ Angelo Cangelosi, University of Manchester & Alan Turing Institute, UK
Lecture#1: Cognitive and Developmental Robotics, part 1
Lecture#2: Cognitive and Developmental Robotics, part 2
+ Swagatam Das, Indian Statistical Institute, Kolkata
Lecture#1: Deep Generative Adversarial Networks and Their Application to Class-imbalanced Learning
Lecture#2: Non-convex Constrained Optimization – Some Advanced Approaches
+ Luca Maria Gambardella, IDSIA Istituto Dalle Molle for Artificial Intelligence, Switzerland
Lecture#1: TBA
Lecture#2: TBA
+ Salvatore Greco, University of Catania, Italy & University of Portsmouth, UK
Lecture#1: Preference Learning in Multicriteria Decision Support
Lecture#2: Evolutionary Multiobjective Optimization Guided by Preference Learning
+ Giuseppe F. Italiano, Luiss University, Italy
Lecture#1: TBA
Lecture#2: TBA
+ Andrea Lodi, Polytechnique Montréal, Canada
Lecture#1: TBA
Lecture#2: TBA
+ Rafael Martì, University of Valencia, Spain
Lecture#1: Optimization in Graph Drawing
Lecture#2: Models and Heuristics in Discrete Diversity Maximization
+ Gabriela Ochoa, University of Stirling, UK
Lecture#1: Fitness Landscape Analysis
Lecture#2: Complex Networks in Search and Optimisation
+ Mauricio Resende, AMAZON, USA
Lecture#1: GRASP with Path-Relinking for Real-World Optimization Problems
Lecture#2: Biased Random-Key Genetic Algorithms with Applications
+ El-Ghazali Talbi, University of Lille 1, France
Lecture#1: Machine learning for metaheuristics
Lecture#2: Automated design of deep neural networks
+ Daniele Vigo, University of Bologna, Italy
Lecture#1: Fast and scalable heuristics for vehicle routing problems
Lecture#2: Integrating machine learning into vehicle routing heuristics
** METAHEURISTICS COMPETITION
All participants to the school will be involved in the “Metaheuristics Competition”, where each of them will must develop a metaheuristic solution on the given problem. The top three of the competition ranking will receive the MESS 2020+1 prize. Students whose algorithm will rank in the top ten of the competition ranking will be invited to submit a manuscript of their work to be published in the special volume MESS 2020+1 of the AIRO Springer Series.
** SHORT TALK & POSTER PRESENTATION
All participants may submit an abstract of their recent results, or works in progress, for presentation and having the opportunities for debate and interact with leaders in the field. Mini-Workshop Organizers and Scientific Committee will review the abstracts and will recommend for the format of the presentation (oral or poster). All abstracts will be published on the electronic hands-out book of the summer school.
The Abstracts must be submitted by *March 5, 2020*.
*See Previous Edition – MESS 2018*
https://www.ants-lab.it/mess2018/
** MORE INFORMATION:
https://www.ANTs-lab.it/mess2020/ — mess.school@ANTs-lab.it
Facebook Group: https://www.facebook.com/groups/MetaheuristicsSchool/
Twitter: https://twitter.com/MESS_school

VeRoLog webinar (with Juan José Salazar González and Grazia Speranza)
2021-05-27 16:00 - 17:30 UTC
Plenary talk: “Split-demand vehicle routing problem: state of the art“
Juan José Salazar González (Universidad de La Laguna)
Followed by an interview with
Grazia Speranza (Università degli Studi di Brescia)
Further information can be found here.

First VeRoLog webinar (with VeRoLog coordinator Daniele Vigo and the organizing committee)
2021-04-29 16:00 - 17:30
Join us for the plenary of VeRoLog coordinator Daniele Vigo on “Integrating Machine Learning into state-of-the-art Vehicle Routing Heuristics” and meet the webinar organizing committee to learn about this new webinar series.
Further information can be found here.

1-week intensive online PhD course in Stochastic Programming
2021-04-19 - 2021-04-23
Cluster for Operations Research, Analytics, and Logistics (CORAL) at Aarhus BSS has the pleasure of announcing a PhD course in Stochastic Programming, which will take place online, 19-23 April 2021.
Lecturer will be Professor Walter Rei, Department of Management and Technology, University of Quebec in Montreal (UQAM).
The general objectives of the course are:

To introduce students to the overall approach that is used to formulate an optimization problem that involves uncertainty as a stochastic program. This approach entails, identifying the specific problems for which stochastic programming is an appropriate method to apply, modelling feasibility and the dynamics of the problem and formulating the objective function.
To present the main specialized solution methods that have been developed to solve stochastic programs. Specifically, a review of the principal exact solution methods and heuristic algorithms will be provided. The focus here will be to present how more efficient solution processes can be obtained by applying a series of tailored strategies, i.e., scenario generation, mathematical decomposition, heuristic search principles, etc.

Professor Sanne Wøhlk from CORAL is the academic organiser, so please feel free to contact her if you have any questions regarding the contents of the course.
Please see the website for more information and registration: DGPE PhD course: Introduction to Stochastic Programming (au.dk)
