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About

I am a researcher at INESC TEC and I have obtained my PhD in Engineering and Industrial Management at FEUP (Faculty of Engineering of the University of Porto). 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 mobility systems, especially in the car rental and car sharing businesses. From the techniques viewpoint, I have been using and developing Matheuristic approaches, which combine heuristics and metaheuristics with mathematical programming. 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 that consider uncertainty issues. Recently, I have also been also extending this interest to include other modelling approaches such as Constraint Programming or Dynamic Programming.

Interest
Topics
Details

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Publications

2022

A Diversity-Based Genetic Algorithm for Scenario Generation

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

Publication
European Journal of Operational Research

Abstract

2021

Carsharing: A review of academic literature and business practices toward an integrated decision-support framework

Authors
Golalikhani, M; Oliveira, BB; Carravilla, MA; Oliveira, JF; Antunes, AP;

Publication
Transportation Research Part E: Logistics and Transportation Review

Abstract
Designing a viable carsharing system in a competitive environment is challenging and often dependent on a myriad of decisions. This paper establishes and presents an integrated conceptual decision-support framework for carsharing systems, encompassing critical decisions that should be made by carsharing organizations and users. To identify the main decisions in a carsharing system, and the inputs and interactions among them, it is crucial to obtain a comprehensive understanding of the current state of the literature as well as the business practices and context. To this aim, a holistic and in-depth literature review is conducted to structure distinct streams of literature and their main findings. Then, we describe some of the key decisions and business practices that are often oversimplified in the literature. Finally, we propose a conceptual decision-support framework that systematizes the interactions between the usually isolated problems in the academic literature and business practices, integrating the perspectives of carsharing organizations and of their users. From the proposed framework, we identify relevant research gaps and ways to bridge them in the future, toward more realistic and applicable research. © 2021 Elsevier Ltd

2021

Understanding carsharing: A review of managerial practices towards relevant research insights

Authors
Golalikhani, M; Oliveira, BB; Carravilla, MA; Oliveira, JF; Pisinger, D;

Publication
Research in Transportation Business & Management

Abstract

2021

Impact of environmental concerns on the capacity-pricing problem in the car rental business

Authors
Queiros, F; Oliveira, BB;

Publication
Journal of Cleaner Production

Abstract
One of the main decisions that a car rental company has to make regards the definition of the fleet size and mix, i.e., the capacity to meet demand. This demand is highly unpredictable and price-sensitive; thus, the definition of the prices charged influences capacity decisions. Moreover, capacity decisions are also linked to other company strategies to meet demand, such as offering upgrades or transferring empty cars between stations. Typically, these problems are tackled focusing on the maximization of profits, disregarding the environmental impacts associated with these decisions. There is a growing need for models and analytical tools that can support decisions considering the trade-off between profit and environmental impact in mobility. Therefore, this work incorporates environmental concerns into the capacity-pricing problem for car rental, proposing a bi-objective model to tackle the trade-off between profit and environmental impact. The Life Cycle Assessment method is applied not only to vehicles but also to fuel to define environmental parameters accurately. Four types of vehicles are considered: internal combustion engine vehicles, hybrids, hybrids plug-in, and electric vehicles. Solving multi-objective models is a computationally challenging problem, which requires efficient and applicable methods. These methods can support policy and business decisions in a real-world context, running different scenarios and evaluating solutions under varying conditions. Due to its efficiency in solving bi-objective models, an Epsilon-constraint method is developed and applied in diverse situations to retrieve managerial insights. The results obtained enable quantifying the feasible trade-offs, overall showing that, on average, with a decrease of 14.44% in financial results, it is possible to obtain a decrease of 63.41% in environmental impact. Additional insights are also retrieved related to the fleet, fuel, prices and demand. © 2021 Elsevier Ltd

2021

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

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

Publication
Optimization Methods and 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.

Supervised
thesis

2021

The art of the deal: Machine learning based trade promotion evaluation

Author
David Branco Viana

Institution
UP-FEUP

2021

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

Author
Masoud Golalikhani

Institution
UP-FEUP

2020

Impact of environmental concerns on the capacity-pricing problem in the car rental business

Author
Fábio Miguel Guimarães Queirós

Institution
UP-FEUP

2020

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

Author
Masoud Golalikhani

Institution
UP-FEUP

2020

A metaheuristic for the capacity-pricing problem in the car rental business

Author
Luis Filipe Ferreira Soares

Institution
UP-FEUP