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About

About

Sou Ana Maria Rodrigues, natural da Maia, distrito do Porto, licenciada em Matemática Aplicada e Computação pela Universidade de Aveiro, Mestre em Métodos Quantitativos Aplicados à Gestão pela Escola de Gestão do Porto da Universidade do Porto (UP) e Doutorada, desde 2014, em Engenharia Industrial e Gestão pela Faculdade de Engenharia da UP com a tese intitulada “Sectores e Rotas na Recolha de Resíduos Sólidos Urbanos”.

Presentemente, sou Professora Adjunta no Instituto Superior de Contabilidade e Administração do Porto do Instituto Politécnico do Porto (ISCAP-PPorto), onde exerço funções desde Dezembro de 1998, e investigadora no INESC TEC – CESE.

Tenho especial interesse de investigação em problemas de otimização combinatória em particular problemas de setorização e problemas de localização e rotas. 

Interest
Topics
Details

Details

003
Publications

2022

A Two-Stage Method to Solve Location-Routing Problems Based on Sectorization

Authors
Teymourifar, A; Rodrigues, AM; Ferreira, JS; Lopes, C; Oliveira, C; Romanciuc, V;

Publication
INNOVATIONS IN INDUSTRIAL ENGINEERING

Abstract

2022

An Integer Programming Approach to Sectorization with Compactness and Equilibrium Constraints

Authors
Romanciuc, V; Lopes, C; Teymourifar, A; Rodrigues, AM; Ferreira, JS; Oliveira, C; Ozturk, EG;

Publication
INNOVATIONS IN INDUSTRIAL ENGINEERING

Abstract

2022

A Comparison Between Optimization Tools to Solve Sectorization Problem

Authors
Teymourifar, A; Rodrigues, AM; Ferreira, JS; Lopes, C;

Publication
Lecture Notes in Networks and Systems

Abstract
In sectorization problems, a large district is split into small ones, usually meeting certain criteria. In this study, at first, two single-objective integer programming models for sectorization are presented. Models contain sector centers and customers, which are known beforehand. Sectors are established by assigning a subset of customers to each center, regarding objective functions like equilibrium and compactness. Pulp and Pyomo libraries available in Python are utilised to solve related benchmarks. The problems are then solved using a genetic algorithm available in Pymoo, which is a library in Python that contains evolutionary algorithms. Furthermore, the multi-objective versions of the models are solved with NSGA-II and RNSGA-II from Pymoo. A comparison is made among solution approaches. Between solvers, Gurobi performs better, while in the case of setting proper parameters and operators the evolutionary algorithm in Pymoo is better in terms of solution time, particularly for larger benchmarks. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

2022

An Application of Preference-Inspired Co-Evolutionary Algorithm to Sectorization

Authors
Öztürk, E; Rocha, P; Sousa, F; Lima, M; Rodrigues, AM; Ferreira, JS; Nunes, AC; Lopes, C; Oliveira, C;

Publication
Lecture Notes in Mechanical Engineering

Abstract

2021

A Comparison Between Simultaneous and Hierarchical Approaches to Solve a Multi-Objective Location-Routing Problem

Authors
Teymourifar, A; Rodrigues, AM; Ferreira, JS;

Publication
AIRO Springer Series

Abstract

Supervised
thesis

2020

Apoio à Decisão em Projetos de Solidariedade Social - Redução do Desperdício Alimentar

Author
João Gil Costa Ramos Pereira

Institution
UP-FEUP

2019

Planeamento de Rotas no Transporte de Alimentos em Projetos de Solidariedade Social

Author
Henrique Daniel Martins Correia

Institution
UP-FEUP

2019

Setorização no Desenho de Rotas - aplicação ao transporte de utentes não urgentes entre as suas residências e um centro hospitalar

Author
Tiago Gaspar Pereira

Institution
UP-FEUP

2018

Front Office de uma empresa de bebidas: Indicadores de Desempenho do Serviço ao Cliente

Author
Bárbara Dias Pereira

Institution
UP-FEUP