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Sobre

Sobre

Sou Professora Catedrática e Subdiretora da Faculdade de Economia da Universidade do Porto (FEP) e membro da direção do LIAAD, Laboratório de Inteligência Artificial e de Apoio à Decisão da UP. O LIAAD é um centro do INESC TEC desde 2007. Sou Agregada em Ciências Empresariais pela FEP (2011), doutora em Management Science pelo Imperial College of London - Business School (2001), mestre em Investigação Operacional pela The London School of Economics and Political Sciences (1994) e Licenciada em Engenharia Eletrotécnica e de Computadores pela Faculdade de Engenharia da Universidade do Porto (1993). Lecionei na The London School of Economics and Political Sciences (1996-99) e fui professora visitante na University of Florida (2007/08) e na Texas A&M University (2015-16). Os meus interesses de investigação centram-se no desenvolvimento e aplicação de técnicas de Investigação Operacional e Inteligência Artificial para auxiliar a tomada de decisão em problemas de gestão em vários domínios (serviços, indústria, logística e transportes), com enfoque em problemas de otimização combinatória. Sou autora de mais de 50 publicações (WoS) e tenho coordenado e estado envolvida em vários projetos de investigação financiados. Sou Associate Editor das revistas "Journal of Combinatorial Optimization" e "Operations Research Forum", ambas da Springer. Colaboro com a FCT na avaliação de bolsas (Painel de Economia e Gestão). Na FEP leciono, maioritariamente em Inglês, disciplinas de Investigação Operacional e Gestão das Operações ao primeiro ciclo, Gestão de Operações, Logística, Análise de Decisão e Otimização aos segundo e terceiros ciclos e estive e estou em vários órgãos (Conselho de Representantes, Conselho Científico, Conselho Pedagógico e Direção do Doutoramento em Gestão e do Mestrado em Modelação, Análise de Dados e Sistemas de Apoio à Decisão, entre outros).

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Dalila Fontes
  • Cargo

    Investigador Coordenador
  • Desde

    01 janeiro 2011
001
Publicações

2023

A multistart biased random key genetic algorithm for the flexible job shop scheduling problem with transportation

Autores
Homayouni, SM; Fontes, DBMM; Goncalves, JF;

Publicação
INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH

Abstract
This work addresses the flexible job shop scheduling problem with transportation (FJSPT), which can be seen as an extension of both the flexible job shop scheduling problem (FJSP) and the job shop scheduling problem with transportation (JSPT). Regarding the former case, the FJSPT additionally considers that the jobs need to be transported to the machines on which they are processed on, while in the latter, the specific machine processing each operation also needs to be decided. The FJSPT is NP-hard since it extends NP-hard problems. Good-quality solutions are efficiently found by an operation-based multistart biased random key genetic algorithm (BRKGA) coupled with greedy heuristics to select the machine processing each operation and the vehicles transporting the jobs to operations. The proposed approach outperforms state-of-the-art solution approaches since it finds very good quality solutions in a short time. Such solutions are optimal for most problem instances. In addition, the approach is robust, which is a very important characteristic in practical applications. Finally, due to its modular structure, the multistart BRKGA can be easily adapted to solve other similar scheduling problems, as shown in the computational experiments reported in this paper.

2023

A Multi-Population BRKGA for Energy-Efficient Job Shop Scheduling with Speed Adjustable Machines

Autores
Homayouni, SM; Fontes, DBMM; Fontes, FACC;

Publicação
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract
Energy-efficient scheduling has become a new trend in industry and academia, mainly due to extreme weather conditions, stricter environmental regulations, and volatile energy prices. This work addresses the energy-efficient Job shop Scheduling Problem with speed adjustable machines. Thus, in addition to determining the sequence of the operations for each machine, one also needs to decide on the processing speed of each operation. We propose a multi-population biased random key genetic algorithm that finds effective solutions to the problem efficiently and outperforms the state-of-the-art solution approaches. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

2023

A hybrid particle swarm optimization and simulated annealing algorithm for the job shop scheduling problem with transport resources

Autores
Fontes, DBMM; Homayouni, SM; Goncalves, JF;

Publicação
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH

Abstract
This work addresses a variant of the job shop scheduling problem in which jobs need to be transported to the machines processing their operations by a limited number of vehicles. Given that vehicles must deliver the jobs to the machines for processing and that machines need to finish processing the jobs before they can be transported, machine scheduling and vehicle scheduling are intertwined. A coordi-nated approach that solves these interrelated problems simultaneously improves the overall performance of the manufacturing system. In the current competitive business environment, and integrated approach is imperative as it boosts cost savings and on-time deliveries. Hence, the job shop scheduling problem with transport resources (JSPT) requires scheduling production operations and transport tasks simultane-ously. The JSPT is studied considering the minimization of two alternative performance metrics, namely: makespan and exit time. Optimal solutions are found by a mixed integer linear programming (MILP) model. However, since integrated production and transportation scheduling is very complex, the MILP model can only handle small-sized problem instances. To find good quality solutions in reasonable com-putation times, we propose a hybrid particle swarm optimization and simulated annealing algorithm (PSOSA). Furthermore, we derive a fast lower bounding procedure that can be used to evaluate the perfor-mance of the heuristic solutions for larger instances. Extensive computational experiments are conducted on 73 benchmark instances, for each of the two performance metrics, to assess the efficacy and efficiency of the proposed PSOSA algorithm. These experiments show that the PSOSA outperforms state-of-the-art solution approaches and is very robust.(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

Energy-efficient job shop scheduling problem with transport resources considering speed adjustable resources

Autores
Fontes, DBMM; Homayouni, SM; Fernandes, JC;

Publicação
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH

Abstract
This work extends the energy-efficient job shop scheduling problem with transport resources by considering speed adjustable resources of two types, namely: the machines where the jobs are processed on and the vehicles that transport the jobs around the shop-floor. Therefore, the problem being considered involves determining, simultaneously, the processing speed of each production operation, the sequence of the production operations for each machine, the allocation of the transport tasks to vehicles, the travelling speed of each task for the empty and for the loaded legs, and the sequence of the transport tasks for each vehicle. Among the possible solutions, we are interested in those providing trade-offs between makespan and total energy consumption (Pareto solutions). To that end, we develop and solve a bi-objective mixed-integer linear programming model. In addition, due to problem complexity we also propose a multi-objective biased random key genetic algorithm that simultaneously evolves several populations. The computational experiments performed have show it to be effective and efficient, even in the presence of larger problem instances. Finally, we provide extensive time and energy trade-off analysis (Pareto front) to infer the advantages of considering speed adjustable machines and speed adjustable vehicles and provide general insights for the managers dealing with such a complex problem.

2023

A bi-objective multi-population biased random key genetic algorithm for joint scheduling quay cranes and speed adjustable vehicles in container terminals

Autores
Fontes, DBMM; Homayouni, SM;

Publicação
FLEXIBLE SERVICES AND MANUFACTURING JOURNAL

Abstract
This work formulates a mixed-integer linear programming (MILP) model and proposes a bi-objective multi-population biased random key genetic algorithm (mp-BRKGA) for the joint scheduling of quay cranes and speed adjustable vehicles in container terminals considering the dual-cycling strategy. Under such a strategy, a combination of loading and unloading containers are handled by a set of cranes (moved between ships and vehicles) and transported by a set of vehicles (transported between the quayside and the storage area). The problem consists of four components: crane scheduling, vehicle assignment, vehicle scheduling, and speed assignment both for empty and loaded journey legs. The results show that an approximated true Pareto front can be found by solving the proposed MILP model and that the mp-BRKGA finds uniformly distributed Pareto fronts, close to the true ones. Additionally, the results clearly demonstrate the advantages of considering speed adjustable vehicles since both the makespan and the energy consumption can be considerably reduced.

Teses
supervisionadas

2021

Mitigação de flutuações PV por limites impostos ao aumento de produção

Autor
João Tiago Teixeira Almeida

Instituição
UP-FEUP

2021

Impact of "Minimum Life on Receipt" on the production of food consumer goods

Autor
Gonçalo Bacelar Xavier Moreno de Sousa

Instituição
UP-FEUP

2021

A temporal analysis of frequent patterns – the impact on  management

Autor
Teresa Raquel do Monte Ramos

Instituição
UP-FEP

2021

Estimação automática do estado de um manipulador robótico sobre-sensorizado

Autor
João Pedro Ribeiro Moreira

Instituição
UP-FEUP

2020

Fault Tolerant Decentralized Deep Neural Networks

Autor
João Carlos Faria Padrão

Instituição
UM