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About

Maria Antónia Carravilla @ FEUP

Maria Antónia Carravilla is a teacher at Faculdade de Engenharia da Universidade do Porto (FEUP) since 1985, visiting professor at Universidade de São Paulo (USP), teacher at Porto Business School in the Executive and Magellan MBA's, and a researcher at INESC-TEC since 1990.

Maria Antónia is the Director of the Doctoral Program in Engineering and Industrial Management (PRODEGI @ FEUP) since 2016.

Maria Antónia Carravilla has been responsible for several R&D contracts with industry, services and public administration. These contracts resulted in reports and decision support systems that proved to be very useful tools for these organizations, leading to long-lasting collaborations with FEUP. The pure research contracts were mainly founded by FCT and are mainly related with the application of constraint programming to the resolution of nesting problems. The R&D contracts and the research contracts were the basis for the theses of several PhD students.

Maria Antónia Carravilla has been a member of the Executive Committee of FEUP for 9 years as Pro-Dean for management and control. She was the Director of the Financial Services and head of the Management Office of FEUP for 7 years. She has been responsible for the Jupiter Project that managed the move of FEUP to the new premises in 2000. She has also been responsible for the projects that resulted in the implementation in FEUP of workflows related with the Financial Services. Within the management office of FEUP she led studies related with indicators for higher education institutions and supervised a masters thesis on sustainability indicators for higher education institutions.

As a teacher at FEUP, Maria Antónia Carravilla has been responsible for several courses related with Operations Research, Operations Management and Logistics that were taught at the BSc, MSc and PhD levels. She has supervised MSc students whose theses were developed in academia as well as in industry.

Maria Antónia Carravilla received in 2009, the first time it has been awarded, FEUP’s Award for Pedagogical Excellence that aims to award the best teacher of FEUP for the past 5 years.

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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

A Benders Decomposition Algorithm for the Berth Allocation Problem

Authors
Barbosa, F; Oliveira, JF; Carravilla, MA; Curcio, EF;

Publication
Springer Proceedings in Mathematics and Statistics

Abstract
In this paper we present a Benders decomposition approach for the Berth Allocation Problem (BAP). Benders decomposition is a cutting plane method that has been widely used for solving large-scale mixed integer linear optimization problems. On the other hand, the Berth Allocation Problem is a NP-hard and large-scale problem that has been gaining relevance both from the practical and scientific points of view. In this work we address the discrete and dynamic version of the problem, and develop a new decomposition approach and apply it to a reformulation of the BAP based on the Heterogeneous Vehicle Routing Problem with Time Windows (HVRPTW) model. In a discrete and dynamic BAP each berth can moor one vessel at a time, and the vessels are not all available to moor at the beginning of the planning horizon (there is an availability time window). Computational tests are run to compare the proposed Benders Decomposition with a state-of-the-art commercial solver. © 2019, Springer Nature Switzerland AG.

2019

Optimality in nesting problems: New constraint programming models and a new global constraint for non-overlap

Authors
Cherri, LH; Carravilla, MA; Ribeiro, C; Toledo, FMB;

Publication
Operations Research Perspectives

Abstract
In two-dimensional nesting problems (irregular packing problems) small pieces with irregular shapes must be packed in large objects. A small number of exact methods have been proposed to solve nesting problems, typically focusing on a single problem variant, the strip packing problem. There are however several other variants of the nesting problem which were identified in the literature and are very relevant in the industry. In this paper, constraint programming (CP) is used to model and solve all the variants of irregular cutting and packing problems proposed in the literature. Three approaches, which differ in the representation of the variable domains, in the way they deal with the core constraints and in the objective functions, are the basis for the three models proposed for each variant of the problem. The non-overlap among pieces, which must be enforced for all the problem variants, is guaranteed through the new global constraint NoOverlap in one of the proposed approaches. Taking the benchmark instances for the strip-packing problem, new instances were generated for each problem variant. Extensive computational experiments were run with these problem instances from the literature to evaluate the performance of each approach applied to each problem variant. The models based on the global constraint NoOverlap performed consistently better for all variants due to the increased propagation and to the low memory usage. The performance of the CP model for the strip packing problem with the global constraint NoOverlap was then compared with the Dotted Board with Rotations using larger instances from the literature. The experiments show that the CP model with global constraint NoOverlap can quickly find good quality solutions in shorter computational times even for large instances. © 2019

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

2018

A dynamic programming approach for integrating dynamic pricing and capacity decisions in a rental context

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

Publication
Springer Proceedings in Mathematics and Statistics

Abstract
Car rental companies have the ability and potential to integrate their dynamic pricing decisions with their capacity decisions. Pricing has a significant impact on demand, while capacity, which translates fleet size, acquisition planning and fleet deployment throughout the network, can be used to meet this price-sensitive demand. Dynamic programming has been often used to tackle dynamic pricing problems and also to deal with similar integrated problems, yet with some significant differences as far as the inventory depletion and replenishment are considered. The goal of this work is to understand what makes the car rental problem different and hinders the application of more common methods. To do so, a discrete dynamic programming framework is proposed, with two different approaches to calculate the optimal-value function: one based on a Mixed Integer Non Linear Program (MINLP) and one based on a Constraint Programming (CP) model. These two approaches are analyzed and relevant insights are derived regarding the (in)ability of discrete dynamic programming to effectively tackle this problem within a rental context when realistically sized instances are considered. © Springer International Publishing AG 2018.

Supervised
thesis

2018

A Framework for Dataflow Orchestration in Lambda Architectures

Author
Rui Filipe de Oliveira Donas-Botto Figueira

Institution
UP-FEUP

2017

O Impacto da Produtividade na Gestão Industrial – Uma análise aplicada ao sector do móvel e do mobiliário de madeira e derivados em Portugal

Author
Jonas André Rodrigues Henriques de Lima

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

2016

Software Defect Classification

Author
João Rui Machado Costa

Institution
UP-FEUP

2016

Decision Support System for a rent-a-car Company

Author
Pedro Ferreira da Silva Vasques de Carvalho

Institution
UP-FEUP