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About

About

I am an Assistant Professor at FEUP (Faculty of Engineering of the University of Porto) and a researcher at INESC TEC, and I obtained my PhD in Engineering and Industrial Management in 2018. My main field of research is Operations Research and Management Science. Within this scientific area, the application area I am studying is fleet management and pricing (and their integration) in shared mobility systems, especially in the car rental and car sharing businesses. From the techniques viewpoint, I have been focusing on optimization, mathematical programming and metaheuristics, as well as other hybrid approaches. I am generally interested in quantitative methods to support real-world decisions in a time- and cost-effective manner, with a special focus on hybridization techniques, especially those considering uncertainty issues.

Interest
Topics
Details

Details

  • Name

    Beatriz Brito Oliveira
  • Role

    Senior Researcher
  • Since

    20th November 2014
006
Publications

2024

Optimisation for operational decision-making in a watershed system with interconnected dams

Authors
Vaz T.G.; Oliveira B.B.; Brandão L.;

Publication
Applied Energy

Abstract
In the energy production sector, increasing the quantity and efficiency of renewable energies, such as hydropower plants, is crucial to mitigate climate change. This paper proposes a new and flexible model for optimising operational decisions in watershed systems with interconnected dams. We propose a systematic representation of watersheds by a network of different connection points, which is the basis for an efficient Mixed-Integer Linear Programming model. The model is designed to be adaptable to different connections between dams in both main and tributary rivers. It supports decisions on power generation, pumping and water discharge, maximising profit, and considering realistic constraints on water use and factors such as future energy prices and weather conditions. A relax-and-fix heuristic is proposed to solve the model, along with two heuristic variants to accommodate different watershed structures and sizes. Methodological tests with simulated instances validate their performance, with both variants achieving results within 1% of the optimal solution faster than the model for the tested instances. To evaluate the performance of the approaches in a real-world scenario, we analyse the case study of the Cávado watershed (Portugal), providing relevant insights for managing dam operations. The model generally follows the actual decisions made in typical situations and flood scenarios. However, in the case of droughts, it tends to be more conservative, saving water unless necessary or profitable. The model can be used in a decision-support system to provide decision-makers with an integrated view of the entire watershed and optimised solutions to the operational problem at hand.

2024

On the benefit of combining car rental and car sharing

Authors
Soppert, M; Oliveira, BB; Angeles, R; Steinhardt, C;

Publication
Journal of Business Economics

Abstract
Car rental and car sharing are two established mobility concepts which traditionally have been offered by specialized providers. Presumably to increase utilization and profitability, most recently, car rental providers began to offer car sharing in addition, and vice versa. To assess and quantify benefits and drawbacks of combining both into a single mobility concept with one common fleet, we consider such combined systems on an aggregate level, replicating demand patterns and rentals throughout a typical week. Our systematic approach reflects that, depending on a provider’s status quo, different business practices exist, for example with regard to the applied revenue management approaches. Methodologically, our analyses base on mathematical optimization. We propose several models that consider the different business practices and degrees to which the respective new mobility concept is offered. To support mobility providers in their strategic decision-making, we derive managerial insights based on numerical studies that use real-life data. © The Author(s) 2024.

2024

Optimizing multi-attribute pricing plans with time- and location-dependent rates for different carsharing user profiles

Authors
Golalikhani, M; Oliveira, BB; Correia, GHD; Oliveira, JF; Carravilla, MA;

Publication
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW

Abstract
One of the main challenges of one-way carsharing systems is to maximize profit by attracting potential customers and utilizing the fleet efficiently. Pricing plans are mid or long-term decisions that affect customers' decision to join a carsharing system and may also be used to influence their travel behavior to increase fleet utilization e.g., favoring rentals on off-peak hours. These plans contain different attributes, such as registration fee, travel distance fee, and rental time fee, to attract various customer segments, considering their travel habits. This paper aims to bridge a gap between business practice and state of the art, moving from unique single-tariff plan assumptions to a realistic market offer of multi-attribute plans. To fill this gap, we develop a mixed-integer linear programming model and a solving method to optimize the value of plans' attributes that maximize carsharing operators' profit. Customer preferences are incorporated into the model through a discrete choice model, and the Brooklyn taxi trip dataset is used to identify specific customer segments, validate the model's results, and deliver relevant managerial insights. The results show that developing customized plans with time- and location-dependent rates allows the operators to increase profit compared to fixed-rate plans. Sensitivity analysis reveals how key parameters impact customer choices, pricing plans, and overall profit.

2023

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

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

Publication
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH

Abstract
In cutting processes, one of the strategies to reduce raw material waste is to generate leftovers that are large enough to return to stock for future use. The length of these leftovers is important since waste is expected to be minimal when cutting these objects in the future. However, in several situations, future demand is unknown and evaluating the best length for the leftovers is challenging. Furthermore, it may not be economically feasible to manage a stock of leftovers with multiple lengths that may not result in minimal waste when cut. In this paper, we approached the cutting stock problem with the possibility of generating leftovers as a two-stage stochastic program with recourse. We approximated the demand levels for the different items by employing a finite set of scenarios. Also, we modeled different decisions made before and after uncertainties were revealed. We proposed a mathematical model to represent this problem and developed a column generation approach to solve it. We ran computational experi-ments with randomly generated instances, considering a representative set of scenarios with a varying probability distribution. The results validated the efficiency of the proposed approach and allowed us to derive insights on the value of modeling and tackling uncertainty in this problem. Overall, the results showed that the cutting stock problem with usable leftovers benefits from a modeling approach based on sequential decision-making points and from explicitly considering uncertainty in the model and the solution method. (c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )

2023

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

Authors
Viana, DB; Oliveira, BB;

Publication
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.

Supervised
thesis

2023

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

Author
Masoud Golalikhani

Institution
INESCTEC

2023

Melhoria da eficiência de fluxo na indústria metalomecânica

Author
Ana Teresa Mansilha Xavier Soares

Institution
INESCTEC

2023

Data-driven Insights for Grocery Retailers: Developing a Serverless Tool for Business Analysis

Author
Enzo Facca Pegorin

Institution
INESCTEC

2023

Implementação de um programa de Transformação Digital e Melhoria Contínua em Grupo Industrial do Setor Mobiliário

Author
Sofia Gabriela Pereira Magalhães

Institution
INESCTEC

2023

Optimisation approaches for operational decision-making in a watershed system with interconnected dams

Author
Tiago Gonçalves Vaz

Institution
INESCTEC