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About

My main area of scientific activity is Operations Research and Management Science. Within Operations Research my main application area are the Cutting and Packing Problems, while from the techniques viewpoint my research is centered in the use and development of Metaheuristics approaches.

Cutting and Packing problems are hard combinatorial optimization problems that arise under several practical contexts, whenever big pieces of raw-material have to be cut into smaller items, or small items have to be packed inside a larger container, so that waste is minimized. These problems include hard geometric constraints when dealing with the optimization layer. I have also worked on Vehicle Routing Problem. My research on Lotsizing and Scheduling problems in industrial contexts mainly builds on my expertise on Metaheuristics.

More recently I have worked on the use of the quantitative methods, provided by Operations Research and Management Science, to support decision making in Higher Education institutions management, which includes workload models, sustainability, institutional benchmarking and assessment and evaluation of institutions and teaching staff.

Interest
Topics
Details

Details

007
Publications

2019

A co-evolutionary matheuristic for the car rental capacity-pricing stochastic problem

Authors
Oliveira, BB; Carravilla, MA; Oliveira, JF; Costa, AM;

Publication
European Journal of Operational Research

Abstract

2019

Data mining based framework to assess solution quality for the rectangular 2D strip-packing problem

Authors
Júnior, AN; Silva, E; Gomes, AM; Soares, C; Oliveira, JF;

Publication
Expert Syst. Appl.

Abstract

2019

Data mining based framework to assess solution quality for the rectangular 2D strip-packing problem

Authors
Neuenfeldt Junior, A; Silva, E; Gomes, M; Soares, C; Oliveira, JF;

Publication
Expert Systems with Applications

Abstract
In this paper, we explore the use of reference values (predictors) for the optimal objective function value of hard combinatorial optimization problems, instead of bounds, obtained by data mining techniques, and that may be used to assess the quality of heuristic solutions for the problem. With this purpose, we resort to the rectangular two-dimensional strip-packing problem (2D-SPP), which can be found in many industrial contexts. Mostly this problem is solved by heuristic methods, which provide good solutions. However, heuristic approaches do not guarantee optimality, and lower bounds are generally used to give information on the solution quality, in particular, the area lower bound. But this bound has a severe accuracy problem. Therefore, we propose a data mining-based framework capable of assessing the quality of heuristic solutions for the 2D-SPP. A regression model was fitted by comparing the strip height solutions obtained with the bottom-left-fill heuristic and 19 predictors provided by problem characteristics. Random forest was selected as the data mining technique with the best level of generalisation for the problem, and 30,000 problem instances were generated to represent different 2D-SPP variations found in real-world applications. Height predictions for new problem instances can be found in the regression model fitted. In the computational experimentation, we demonstrate that the data mining-based framework proposed is consistent, opening the doors for its application to finding predictions for other combinatorial optimisation problems, in particular, other cutting and packing problems. However, how to use a reference value instead of a bound, has still a large room for discussion and innovative ideas. Some directions for the use of reference values as a stopping criterion in search algorithms are also provided. © 2018 Elsevier Ltd

2018

A new Load Balance Methodology for Container Loading Problem in Road Transportation

Authors
Ramos, AG; Silva, E; Oliveira, JF;

Publication
European Journal of Operational Research

Abstract

2018

Allocating products on shelves under merchandising rules: Multi-level product families with display directions

Authors
Bianchi Aguiar, T; Silva, E; Guimardes, L; Carravilla, MA; Oliveira, JF;

Publication
Omega (United Kingdom)

Abstract
Retailers’ individual products are categorized as part of product families. Merchandising rules specify how the products should be arranged on the shelves using product families, creating more structured displays capable of increasing the viewers’ attention. This paper presents a novel mixed integer programming formulation for the Shelf Space Allocation Problem considering two innovative features emerging from merchandising rules: hierarchical product families and display directions. The formulation uses single commodity flow constraints to model product sequencing and explores the product families’ hierarchy to reduce the combinatorial nature of the problem. Based on the formulation, a mathematical programming-based heuristic was also developed that uses product families to decompose the problem into a sequence of sub-problems. To improve performance, its original design was adapted following two directions: recovery from infeasible solutions and reduction of solution times. A new set of real case benchmark instances is also provided, which was used to assess the formulation and the matheuristic. This approach will allow retailers to efficiently create planograms capable of following merchandising rules and optimizing shelf space revenue. © 2017 Elsevier Ltd

Supervised
thesis

2017

The Two-Dimensional Rectangular Strip Packing Problem

Author
Álvaro Luiz Neuenfeldt Júnior

Institution
UP-FEUP

2017

Fleet and revenue management in car rental: quantitative approaches for optimization under uncertainty

Author
Maria Beatriz Brito Oliveira

Institution
UP-FEUP

2017

A Different Approach on Reverse Logistics - A Retailer’s Case Study

Author
Raquel Vieira da Silva

Institution
UP-FEUP

2017

Sistema de apoio à decisão para a elaboração de mapas de exames no ensino superior

Author
Raphaël Martins Cordeiro

Institution
UP-FEUP

2016

Resolução de problemas de nesting usando técnicas de programação não-linear

Author
Jeinny Maria Peralta Polo

Institution
Outra