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About

About

I am Full Professor 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 60 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).

I was FEP Vice-Dean and currently I am the Diretor of ISFEP – Institute of Resarch and Services of FEP.

Interest
Topics
Details

Details

  • Name

    Dalila Fontes
  • Role

    Research Coordinator
  • Since

    01st January 2011
  • Nationality

    Portugal
  • Contacts

    +351220402963
    dalila.fontes@inesctec.pt
001
Publications

2024

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

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

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

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

Publication
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

Authors
Homayouni, SM; Fontes, DBMM; Fontes, FACC;

Publication
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

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

Publication
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

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

Authors
Fontes, DBMM; Homayouni, SM;

Publication
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.

Supervised
thesis

2023

Energy-Aware Job Shop Scheduling: Optimizing Production Efficiency through Machine Power Saving Strategies

Author
Pedro Sollari Allegro de Soveral Casal

Institution
UP-FEP

2022

Sequenciamento sustentável - abordagens heurísticas para operações deterioráveis ou perecíveis

Author
Daniel Santos do Carmo Roseta

Institution
UP-FEUP

2021

Optimizing inventory management with prepacks

Author
Ana Catarina Costa Azevedo

Institution
UP-FEP

2021

Automatic Service Containerization with Docker

Author
João Carlos Cardoso Maduro

Institution
UP-FEUP

2020

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

Author
Cristiana Filipa Guedes Cardoso

Institution
UP-FEP