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O João Fernandes é um investigador no LIAAD e aluno do Programa Doutoral em Engenharia e Gestão Industrial na FEUP. Os seus tópicos de interesse são Investigação Operacional, Metaheurísticas, Análise Preditiva e Machine Learning. De momento está a investigar problemas de Agendamento de Produção, especificamente Eficiência Energética em problemas de Job Shop Scheduling. O João tem um Mestrado Integrado em Engenharia Industrial e Gestão na FEUP. Tem também dois anos de experiência profissional em Data Science, tendo trabalhado anteriormente na Glintt e na NOS Comunicações

de interesse


  • Nome

    João Chaves Fernandes
  • Cluster

  • Cargo

    Estudante Externo
  • Desde

    01 agosto 2018


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

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


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.


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

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


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.


Mathematical modelling of multi-product ordering in three-echelon supply chain networks

Homayouni, SM; Khayyambashi, A; Fontes, DBMM; Fernandes, JC;

Proceedings of the International Conference on Industrial Engineering and Operations Management

This paper proposes a mixed integer linear programming model for a multi-product ordering in a three-echelon supply chain network, where multiple manufacturers supply multiple warehouses with multiple products, which in turn distribute the products to the multiple retailers involved. The model considers practical production constraints such as production capacity, backorder allowances, and economically-viable minimum order quantities. Numerical computations show that the model can efficiently solve small-sized problem instances. © 2019, IEOM Society International.