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Sobre

Sobre

Sou investigadora no INESC TEC e doutorada em Engenharia e Gestão Industrial pela FEUP (Faculdade de Engenharia da Universiade do Porto). O meu campo de investigação principal é Investigação Operacional e Gestão. Dentro desta área científica, a área de aplicação que tenho vindo a estudar lida com problemas de gestão de frota e pricing (e a integração destes dois problemas) em sistemas de mobilidade, especialmente no setor de aluguer de automóveis e de car sharing. Tenho vindo a usar e desenvolver matheurísticas, técnicas que combinam heurísticas e metaheurísticas com programação matemática. Os meus interesses são, no geral, métodos quantitativos que apoiem decisões num contexto real, de forma eficiente em termos de tempos e custos, com um foco especial em técnicas híbridas, especialmente considerando incerteza. Recentemente, tenho vindo a extender este interesse para outras abordagens de modelação como Constraint Programming e Dynamic Programming.

Tópicos
de interesse
Detalhes

Detalhes

004
Publicações

2023

The Art of the Deal: Machine Learning Based Trade Promotion Evaluation

Autores
Viana, DB; Oliveira, BB;

Publicação
Springer Proceedings in Mathematics and Statistics

Abstract
Trade promotions are complex marketing agreements between a retailer and a manufacturer aiming to drive up sales. The retailer proposes numerous sales promotions that the manufacturer partially supports through discounts and deductions. In the Portuguese consumer packaged goods (CPG) sector, the proportion of price-promoted sales to regular-priced sales has increased significantly, making proper promotional planning crucial in ensuring manufacturer margins. In this context, a decision support system was developed to aid in the promotional planning process of two key product categories of a Portuguese CPG manufacturer. This system allows the manufacturer’s commercial team to plan and simulate promotional scenarios to better evaluate a proposed trade promotion and negotiate its terms. The simulation is powered by multiple gradient boosting machine models that estimate sales for a given promotion based solely on the scarce data available to the manufacturer. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

2022

A Diversity-Based Genetic Algorithm for Scenario Generation

Autores
Oliveira, BB; Carravilla, MA; Oliveira, JF;

Publicação
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH

Abstract

2022

A C++ application programming interface for co-evolutionary biased random-key genetic algorithms for solution and scenario generation

Autores
Oliveira, BB; Carravilla, MA; Oliveira, JF; Resende, MGC;

Publicação
OPTIMIZATION METHODS & SOFTWARE

Abstract
This paper presents a C++ application programming interface for a co-evolutionary algorithm for solution and scenario generation in stochastic problems. Based on a two-space biased random-key genetic algorithm, it involves two types of populations that are mutually impacted by the fitness calculations. In the solution population, high-quality solutions evolve, representing first-stage decisions evaluated by their performance in the face of the scenario population. The scenario population ultimately generates a diverse set of scenarios regarding their impact on the solutions. This application allows the straightforward implementation of this algorithm, where the user needs only to define the problem-dependent decoding procedure and may adjust the risk profile of the decision-maker. This paper presents the co-evolutionary algorithm and structures the interface. We also present some experiments that validate the impact of relevant features of the application. © 2021 Informa UK Limited, trading as Taylor & Francis Group.

2022

First-mile logistics parcel pickup: Vehicle routing with packing constraints under disruption

Autores
Gimenez Palacios, I; Parreno, F; Alvarez Valdes, R; Paquay, C; Oliveira, BB; Carravilla, MA; Olivera, JF;

Publicação
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW

Abstract

2022

A stochastic programming approach to the cutting stock problem with usable leftovers

Autores
Cherri, AC; Cherri, LH; Oliveira, BB; Oliveira, JF; Carravilla, MA;

Publicação
European Journal of Operational Research

Abstract

Teses
supervisionadas

2022

Developing a Platform for Product Catalogue Management and Data Analysis of Food Retailers

Autor
Francisco Jorge da Silva Neves e Queiroz Machado

Instituição
UP-FEUP

2022

Time Series Forecasting and Categorization: an Empirical Study on Intermittent and Lumpy Demand

Autor
Rodrigo Cardoso Miranda Santos

Instituição
UP-FEUP

2022

An integrated decision-support framework towards incorporating practical pricing decisions into carsharing systems

Autor
Masoud Golalikhani

Instituição
UP-FEUP

2022

Solving the Facility Layout Problem In The Wine industry

Autor
Bernardo Campos Leichsenring Franco

Instituição
UP-FEUP

2022

Mobile Charging Stations in Electric Shared Micromobility

Autor
Daniela Figueiredo Ângelo

Instituição
UP-FEUP