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About

I am an Associate Professor with habilitation at Faculdade de Economia da Universidade do Porto (FEP) and board member of LIAAD, Laboratório de Inteligência Artificial e de Apoio à Decisão of UP. LIAAD is a unit of INESC TEC since 2007. I am Ph.D. in Management Science, Imperial College of London - Business School (2000), MSc. In Operational Research, The London School of Economics and Political Sciences (1994), and I have a first degree (5-years) in Electrical Engineering and Computer, Faculdade de Engenharia da Universidade do Porto (1993). Teaching Assistant at The London School of Economics and Political Sciences (1996-99). Visiting Professor at University of Florida (2007/08) and at Texas A& M University (2015-16). My research interests include developing and applying Operational Research and Artificial Intelligence techniques for decision support in Management problems (including production, storage, logistics and transportation, and services), with particular interest on combinatorial optimization applications. I have authored over 50 publications (WoS) and I have been coordinating and participating in several research projects. I am Associate Editor of Journal of Combinatorial Optimization and Operations Research Forum (both Springer). At FEP I have been lecturing, mainly in English, Operational Research and Operations Management to undergraduates, Research and Operations Management , Logistics, Decision Analysis, and Optimization to Master students and Ph.D. candidates. At FEP have also been involved in several boards and councils (Representative Council, Scientific Board, Pedagogical Board, Scientific Committee of the Ph.D. in Management and of the Master in Modeling, Data Analysis and Decision Support Systems, among others).

Interest
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Details

Details

  • Name

    Dalila Fontes
  • Cluster

    Computer Science
  • Role

    Research Coordinator
  • Since

    01st January 2011
003
Publications

2022

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

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

Publication
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

Authors
Homayouni, SM; Fontes, DBMM;

Publication
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

A Hybrid Particle Swarm Optimization and Simulated Annealing Algorithm for the Job Shop Scheduling Problem with Transport Resources

Authors
Fontes, DB; Homayouni, SM; Gonçalves, JF;

Publication
European Journal of Operational Research

Abstract

2021

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

Authors
Homayouni, SM; Fontes, DBMM;

Publication
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.

2020

A Lagrangian Bound on the Clique Number and an Exact Algorithm for the Maximum Edge Weight Clique Problem

Authors
Hosseinian, S; Fontes, DBMM; Butenko, S;

Publication
INFORMS JOURNAL ON COMPUTING

Abstract

Supervised
thesis

2021

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

Author
Helena Patrícia Campos da Silva

Institution
UP-FEUP

2020

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

Author
Cindy dos Santos Alves

Institution
UP-FEP

2020

Flexible Job Shop scheduling with Transportation

Author
Filipe Lopes Laginha da Palma

Institution
UP-FEP

2020

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

Author
Cristiana Filipa Guedes Cardoso

Institution
UP-FEP

2020

Design of a Supply Chain Network with Financial Considerations.

Author
Maria Alexandra Teixeira Borges Vieira Pouzada

Institution
UP-FEP