The Methods and Models of Optimization (MeMO) laboratory at Federal University of São Paulo opens a post-doctoral research position in “Adaptive Metaheuristic applied to dial-a-ride problems and variants”. The selected candidate will work at the Institute of Science and Technology (ICT-UNIFESP), São José dos Campos, SP/Brazil.
The study of DARP variants can contribute to the decision making of routing problems during the effects of COVID-19. People who need transport on demand need to be protected. In this way, requests must be met minimizing the time inside the vehicle, using reduced capacity, prioritizing that the vehicles have the minimum number of people together, etc.
São Paulo Research Foundation provides the financial support according to http://www.fapesp.br/en/5427. The postdoctoral fellowship includes a monthly stipend of R$ 7.373,10 (about USD 1,800 or EU$ 1,600). Financial support can also be provided to cover transportation expenses as to the move to São José dos Campos – Brazil. An extra grant is also provided to cover participation in highly relevant conferences and workshops, as well as research trips (limited to 15% of the annual amount of the fellowship). The position is for two years.
REQUIREMENTS: Applicants should have PhD in Computer Science, Operations Research or in a related field with some experience with the C\C++ language, metaheristics and machine learning. Candidates must have got their PhD in the last 5 years.
APPLICATION: The candidate must send by email until May 15, 2020 the following documents:
* Curriculum vitae with a list of publications, and previous experience.
* A recommendation letter from a previous supervisor.
* A motivation letter for the application.
All documents must be sent to Prof. Antonio Chaves (firstname.lastname@example.org)
GENERAL PROJECT: Development of a Hybrid Metaheuristic with Adaptive Control Flow and Parameters
SUPERVISOR: Antônio Augusto Chaves (http://lattes.cnpq.br/
DESCRIPTION: The study of efficient metaheuristics to solve optimization problems has been the subject of much research by the scientific community. To obtain good results in terms of solution quality and computational time it is important to have a good configuration of the metaheuristic. This process of specifying control flow and parameter values of a method is a dificult task. Hence, this project has as its main idea the development and improvement of the adaptive Biased Random-key Genetic Algorithm (A-BRKGA) method to choose which components will be used and in which sequence (A-BRKGA flow) and which parameters to use while an instance of a problem is being solved. To this end, machine learning techniques and adaptive and reactive mechanisms will be studied to construct an A-BRKGA with online configuration of parameters and control flow. The goal is to generate an efficient algorithm to solve combinatorial optimization problems and make the code easy to reuse. In order to evaluate the proposed method four optimization problems with industrial and logistical applications will be studied: field technician scheduling problem, multicommodity traveling salesman problem with priority prizes, two-stage capacitated facility location problem and facility location computational tests will use available instances in the literature and real case studies. The method will be compared with state-of-the-art algorithms through statistical analysis.