Academic Background

Federal University of São Carlos

Doctor's Degree • Production Engineering/Operations Research

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Duration: Mar.2025 - Ongoing
Thesis' topic: TBD
Supervisor: Prof. Dr. Pedro Munari

University of Bath, School of Management

Visiting Researcher

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Duration: Oct.2023 - Mar.2023
Supervisor: Prof. Dr. Maria Battarra
Funding: Grant n.2023/08678-8, São Paulo Research Fundation (FAPESP)

Federal University of São Carlos

Master's Degree • Production Engineering/Operations Research

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Duration: Aug.2022 - Jan.2025
Dissertation: The robust pickup and delivery problem with time windows (https://hdl.handle.net/20.500.14289/21368)
Supervisors: Prof. Dr. Pedro Munari
Prof. Dr. Luciano Costa (UFPB)
Funding: Grant n.2022/10993-6, São Paulo Research Fundation (FAPESP)

Federal University of Goiás

Bachelor Degree • Production Engineering

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Final Work Title: An efficiency and robustness analysis of warm-start mathematical models for idle and waiting times optimization in the flow shop
Duration: Mar.2017 - May.2022
Supervisor: Prof. Dr. Helio Fuchigami

Work Experiences

Grupo BR Aço
Financial Planning & Analysis • Analyst (Sep.2021 - Jul.2022)

Cargill
Research & Development • Intern (Aug.2020 - Aug.2021)

submitted papers

Abreu, A., Battarra, M., Costa, L., Munari, P. (2025).
The robust pickup and delivery problem with time windows. Submitted.

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Repository: Optimization Online
Abstract: This study addresses the robust pickup and delivery problem with time windows (RPDPTW), in which uncertainty in demands and travel times is modelled using robust optimisation. The RPDPTW involves determining the least-cost routes to serve transportation requests from origins to destinations, while respecting vehicle capacity and time window constraints under all anticipated realisations of uncertain data. Two robust counterpart formulations are proposed. The first employs the linearisation of recursive equations to produce a compact formulation, suitable for implementation with general-purpose mixed-integer programming solvers. The second relies on a cutting-plane approach, incorporated into a tailored branch-and-cut (B&C) algorithm. Extensive computational experiments were conducted to evaluate the proposed methods across diverse instance configurations. While the compact formulation performed effectively in some cases, the B&C algorithm solved a larger number of instances to optimality and consistently provided high-quality solutions under uncertainty. Robust solutions significantly reduced the risk of infeasibility compared to deterministic solutions, with modest increases in routing costs. In addition, we provide managerial insights by analysing the results of Monte Carlo simulations across different instance characteristics.
Keywords: Routing, pickup and delivery problem, time windows, demand uncertainty, travel time uncertainty.

Publications

Fuchigami, H. Y. & Abreu, A. P. (2024).
Innovative optimization algorithms for large-sized industrial scheduling problems. Brazilian Archives of Biology and Technology.

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Abstract The problem of minimizing total quadratic completion time in flow shops presents a significant challenge in industries such as chemical, metallurgical, and ceramic manufacturing. Initially investigated by Ren and coauthors (2016), this problem addresses the need to balance intermediate inventory reduction with maximizing resource utilization, particularly in multi-objective scenarios. We proposed two innovative metaheuristics (Covid and CHIO algorithms) and a mathematical optimization model. Evaluations were conducted across two industrial settings and three additional benchmarks from existing literature. Through the statistical analysis and performance profiling, our findings indicate that the Covid and CHIO algorithms outperform the Differential Evolution of Ren and coauthors (2016) and the Iterated Greedy Algorithm of Pan and Ruiz (2012), as well as other techniques. Notably, the proposed Covid and CHIO algorithms achieved an average relative deviation from the optimal solution of 0.41% and 0.23%, respectively. Furthermore, they consistently outperformed other methods, securing the best solution in at least 12.5% of instances across all benchmarks, with their worst solutions closer to the best solutions than those produced by alternative approaches.
Keywords: flow shop, metaheuristics, mathematical optimization, total quadratic completion time, stochastic local search

Abreu, A. P., & Fuchigami, H. Y. (2022).
An efficiency and robustness analysis of warm-start mathematical models for idle and waiting times optimization in the flow shop. Computers & Industrial Engineering.

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Abstract This paper aims to address current manufacturing problems such as high buffers and energy waste by considering a novel performance measure, the sum of the core and front idle time of each machine and the core waiting time of each job, within the permutation flow shop scheduling optimization. Four new mixed-integer linear programming (MILP) models and three warm-start procedures were implemented in 240 instances of a widely used benchmark. The results of computational experiments were evaluated using performance profiles, considering computing times and optimality gaps. Among the models, the solver achieved better results with the position-based models than with the sequence-based models, achieving 25% more optimal solutions, being more efficient and robust. Regarding the warm-start strategies, we highlight the procedure with the longest processing time (LPT) heuristic as the best method among the seven studied.
Keywords: Flow shop scheduling, idle time optimization, waiting time optimization, mixed-integer linear programming models, heuristics, warm-start.

Hi! I'm Alex Abreu

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Academic Background

Federal University of São Carlos

University of Bath, School of Management

Federal University of São Carlos

Federal University of Goiás

Work Experiences

submitted papers

Publications

All models are wrong, but some are useful.

George Box