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Sobre

Sobre

Sou Professora Associada com Agregação 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
  • Cluster

    Informática
  • Cargo

    Investigador Coordenador
  • Desde

    01 janeiro 2011
003
Publicações

2022

Energy-Efficient Scheduling in Job Shop Manufacturing Systems: A Literature Review

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

Publicação
SUSTAINABILITY

Abstract
Energy efficiency has become a major concern for manufacturing companies not only due to environmental concerns and stringent regulations, but also due to large and incremental energy costs. Energy-efficient scheduling can be effective at improving energy efficiency and thus reducing energy consumption and associated costs, as well as pollutant emissions. This work reviews recent literature on energy-efficient scheduling in job shop manufacturing systems, with a particular focus on metaheuristics. We review 172 papers published between 2013 and 2022, by analyzing the shop floor type, the energy efficiency strategy, the objective function(s), the newly added problem feature(s), and the solution approach(es). We also report on the existing data sets and make them available to the research community. The paper is concluded by pointing out potential directions for future research, namely developing integrated scheduling approaches for interconnected problems, fast metaheuristic methods to respond to dynamic scheduling problems, and hybrid metaheuristic and big data methods for cyber-physical production systems.

2022

Energy-Efficient Scheduling of Intraterminal Container Transport

Autores
Homayouni, SM; Fontes, DBMM;

Publicação
Springer Optimization and Its Applications

Abstract
Maritime transportation has been, historically, a major factor in economic development and prosperity since it enables trade and contacts between nations. The amount of trade through maritime transport has increased drastically; for example, about 90% of the European Union’s external trade and one-third of its internal trade depend on maritime transport. Major ports, typically, incorporate multiple terminals serving containerships, railways, and other forms of hinterland transportation and require interterminal and intraterminal container transport. Many factors influence the productivity and efficiency of ports and hence their economic viability. Moreover, environmental concerns have been leading to stern regulation that requires ports to reduce, for example, greenhouse gas emissions. Therefore, port authorities need to balance economic and ecological objectives in order to ensure sustainable growth and to remain competitive. Once a containership moors at a container terminal, several quay cranes are assigned to the ship to load/unload the containers to/from the ship. Loading activities require the containers to have been previously made available at the quayside, while unloading ones require the containers to be removed from the quayside. The containers are transported between the quayside and the storage yard by a set of vehicles. This chapter addresses the intraterminal container transport scheduling problem by simultaneously scheduling the loading/unloading activities of quay cranes and the transport (between the quayside and the storage yard) activities of vehicles. In addition, the problem includes vehicles with adjustable travelling speed, a characteristic never considered in this context. For this problem, we propose bi-objective mixed-integer linear programming (MILP) models aiming at minimizing the makespan and the total energy consumption simultaneously. Computational experiments are conducted on benchmark instances that we also propose. The computational results show the effectiveness of the MILP models as well as the impact of considering vehicles with adjustable speed, which can reduce the makespan by up to 16.2% and the total energy consumption by up to 2.5%. Finally, we also show that handling unloading and loading activities simultaneously rather than sequentially (the usual practice rule) can improve the makespan by up to 34.5% and the total energy consumption by up to 18.3%. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

2022

Job-shop scheduling-joint consideration of production, transport, and storage/retrieval systems

Autores
Fontes, DBMM; Homayouni, SM; Resende, MGC;

Publicação
JOURNAL OF COMBINATORIAL OPTIMIZATION

Abstract
This paper proposes a new problem by integrating the job shop scheduling, the part feeding, and the automated storage and retrieval problems. These three problems are intertwined and the performance of each of these problems influences and is influenced by the performance of the other problems. We consider a manufacturing environment composed of a set of machines (production system) connected by a transport system and a storage/retrieval system. Jobs are retrieved from storage and delivered to a load/unload area (LU) by the automated storage retrieval system. Then they are transported to and between the machines where their operations are processed on by the transport system. Once all operations of a job are processed, the job is taken back to the LU and then returned to the storage cell. We propose a mixed-integer linear programming (MILP) model that can be solved to optimality for small-sized instances. We also propose a hybrid simulated annealing (HSA) algorithm to find good quality solutions for larger instances. The HSA incorporates a late acceptance hill-climbing algorithm and a multistart strategy to promote both intensification and exploration while decreasing computational requirements. To compute the optimality gap of the HSA solutions, we derive a very fast lower bounding procedure. Computational experiments are conducted on two sets of instances that we also propose. The computational results show the effectiveness of the MILP on small-sized instances as well as the effectiveness, efficiency, and robustness of the HSA on medium and large-sized instances. Furthermore, the computational experiments clearly shown that importance of optimizing the three problems simultaneous. Finally, the importance and relevance of including the storage/retrieval activities are empirically demonstrated as ignoring them leads to wrong and misleading results.

2021

A MILP Model for Energy-Efficient Job Shop Scheduling Problem and Transport Resources

Autores
Homayouni, SM; Fontes, DBMM;

Publicação
ADVANCES IN PRODUCTION MANAGEMENT SYSTEMS: ARTIFICIAL INTELLIGENCE FOR SUSTAINABLE AND RESILIENT PRODUCTION SYSTEMS, APMS 2021, PT I

Abstract
This work addresses the energy-efficient job shop scheduling problem and transport resources with speed scalable machines and vehicles which is a recent extension of the classical job shop problem. In the environment under consideration, the speed with which machines process production operations and the speed with which vehicles transport jobs are also to be decided. Therefore, the scheduler can control both the completion times and the total energy consumption. We propose a mixed-integer linear programming model that can be efficiently solved to optimality for small-sized problem instances. © 2021, IFIP International Federation for Information Processing.

2021

Production and transport scheduling in flexible job shop manufacturing systems

Autores
Homayouni, SM; Fontes, DBMM;

Publicação
JOURNAL OF GLOBAL OPTIMIZATION

Abstract

Teses
supervisionadas

2021

Prototype of a mandibular advancement device with microsensors for sleep apnea syndrome and snoring

Autor
Helena Patrícia Campos da Silva

Instituição
UP-FEUP

2020

Vehicle Routing Problem with multiple trips and time constraints (VRPMTTC): A Case Study

Autor
Cindy dos Santos Alves

Instituição
UP-FEP

2020

Flexible Job Shop scheduling with Transportation

Autor
Filipe Lopes Laginha da Palma

Instituição
UP-FEP

2020

Indústria 4.0 e a Responsabilidade Social na indústria transformadora portuguesa: um estudo exploratório

Autor
Cristiana Filipa Guedes Cardoso

Instituição
UP-FEP

2020

Design of a Supply Chain Network with Financial Considerations.

Autor
Maria Alexandra Teixeira Borges Vieira Pouzada

Instituição
UP-FEP