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Detalhes

Detalhes

  • Nome

    Luís Guimarães
  • Cargo

    Investigador Sénior
  • Desde

    01 julho 2013
  • Nacionalidade

    Portugal
  • Contactos

    +351 22 209 4190
    luis.guimaraes@inesctec.pt
020
Publicações

2023

Mathematical Formulation of Markov Decision Process to Address Maintenance Policy in Photovoltaic Farms

Autores
Bacalhau, ET; Barbosa, F; Casacio, L; Yamada, F; Guimarães, L;

Publicação
Proceeding of the 33rd European Safety and Reliability Conference

Abstract

2023

Multiobjective Evolutionary Clustering to Enhance Fault Detection in a PV System

Autores
Yamada, L; Rampazzo, P; Yamada, F; Guimarães, L; Leitão, A; Barbosa, F;

Publicação
Springer Proceedings in Mathematics and Statistics

Abstract
Data clustering combined with multiobjective optimization has become attractive when the structure and the number of clusters in a dataset are unknown. Data clustering is the main task of exploratory data mining and a standard statistical data analysis technique used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. This project analyzes data to extract possible failure patterns in Solar Photovoltaic (PV) Panels. When managing PV Panels, preventive maintenance procedures focus on identifying and monitoring potential equipment problems. Failure patterns such as soiling, shadowing, and equipment damage can disturb the PV system from operating efficiently. We propose a multiobjective evolutionary algorithm that uses different distance functions to explore the conflicts between different perspectives of the problem. By the end, we obtain a non-dominated set, where each solution carries out information about a possible clustering structure. After that, we pursue a-posteriori analysis to exploit the knowledge of non-dominated solutions and enhance the fault detection process of PV panels. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

2023

Electric charging demand forecast and capture for infrastructure placement using gravity modelling: a case study

Autores
Rodrigues, G; Barbosa, F; Schuller, P; Silva, D; Pereira, J; Azevedo, R; Guimaraes, L;

Publicação
2023 IEEE 26TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS, ITSC

Abstract
As the demand for electric charging accelerates, so does the stress on the relatively insufficient public charging infrastructure. To appropriately manage and scale charging infrastructure, there is a need for support tools capable of predicting the utilization and sales of charging stations, as well as the traffic flow of users from their original location to the charging stations. Therefore, this article proposes a generic methodology for infrastructure placement, namely forecasting demand and predicting its flow to the supply points. The methodology is applied in a case study to the electric charging grid of Portugal with real data, in the context of the needs of a particular charging point operator (CPO). Demand is first forecasted at a high-granularity level with a demand disaggregation model, followed by its capture by the grid of chargers using a parameterized gravity model. Validation is performed by comparing actual with predicted sales per charging station. Adequate visualizations to support decision-making are presented.

2022

The multi-product inventory-routing problem with pickups and deliveries: Mitigating fluctuating demand via rolling horizon heuristics br

Autores
Neves Moreira, F; Almada Lobo, B; Guimaraes, L; Amorim, P;

Publicação
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW

Abstract
In this paper, we explore the value of considering simultaneous pickups and deliveries inmulti-product inventory-routing problems both with deterministic and uncertain demand. Wepropose a multi-commodity, develop an exact branch-and-cut algorithm with patching heuristicsto efficiently tackle this problem, and provide insightful analyses based on optimal plans. Thesimplicity of the proposed approach is an important aspect, as it facilitates its usage in practice,opposed to complicated stochastic or probabilistic methods. The computational experimentssuggest that in the deterministic demand setting, pickups are mainly used to balance initialinventories, achieving an average total cost reduction of 1.1%, while transshipping 2.4% oftotal demand. Under uncertain demand, pickups are used extensively, achieving cost savings of up to 6.5% in specific settings. Overall, our sensitivity analysis shows that high inventory costsand high degrees of demand uncertainty drive the usage of pickups, which, counter-intuitively, are not desirable in every case

2021

Improving picking performance at a large retailer warehouse by combining probabilistic simulation, optimization, and discrete-event simulation

Autores
Amorim Lopes, M; Guimaraes, L; Alves, J; Almada Lobo, B;

Publicação
INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH

Abstract
Distribution warehouses are a critical part of supply chains, representing a nonnegligible share of the operating costs. This is especially true for unautomated, labor-intensive warehouses, partially due to time-consuming activities such as picking up items or traveling. Inventory categorization techniques, as well as zone storage assignment policies, may help in improving operations, but may also be short-sighted. This work presents a three-step methodology that uses probabilistic simulation, optimization, and event-based simulation (SOS) to analyze and experiment with layout and storage assignment policies to improve the picking performance. In the first stage, picking performance is estimated under different storage assignment policies and zone configurations using a probabilistic model. In the second stage, a mixed integer optimization model defines the overall warehouse layout by selecting the configuration and storage assignment policy for each zone. Finally, the optimized layout solution is tested under demand uncertainty in the third, final simulation phase, through a discrete-event simulation model. The SOS methodology was validated with three months of operational data from a large retailer's warehouse, successfully illustrating how it may be successfully used for improving the performance of a distribution warehouse.

Teses
supervisionadas

2023

Process improvement by data-driven decision-making

Autor
Maria Alexandra Ramalho de Oliveira

Instituição
UP-FEUP

2022

Otimização do Stock de Consumíveis e Peças de Reserva: Automatização das Encomendas com Base em Modelos Preditivos

Autor
José Miguel Borges Balio Duarte Pinto

Instituição
UP-FEUP

2022

O&M optimization for multi-asset offshore renewable energy parks

Autor
Francisco José Vieira Parente

Instituição
UP-FEUP

2022

LazyFS: A file system for assessing applications data durability

Autor
João Pedro Rodrigues Azevedo

Instituição
UM

2022

Sistema de Formação Integrado para Enfermeiros e Auxiliares de Ação Médica em Bloco Operatório

Autor
Esmeralda Alves Monteiro Ferreira da Cruz

Instituição
UP-FEUP