Susana Isabel Carvalho Relvas

Professor Associado

Instituto Superior Técnico

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WEBSITE ORCID CIÊNCIA VITAE ResearcherID Scopus GScholar

Susana Relvas is an Associate Professor at the Department of Engineering and Management (DEG) of Instituto Superior Técnico (IST), University of Lisbon (UL), Portugal. She obtained her PhD in 2008, Industrial Engineering and Management from IST. She is currently IST Deputy Director for Funding and Competitiveness, Vice-President of DEG, Executive Board Member of IST TagusPark Campus, Coordinator of the Lifelong Learning Programme on Engineering and Management @ Técnico+, and Deputy Director of RENE @ KIC InnoENergy. Her research interests are Logistics, Operations Management, and Supply Chain Management applied to topics such as the Retail Industry, Agri-Food Supply Chains, Manufacturing, and Energy Supply Chains, linking to Human-Centred Approaches, Sustainability, and Resilience. She is settling the Logistics Lab at IST TagusPark in coordination with Tânia Ramos, which will focus on research and teaching in Logistics, centred on innovation and R&D development and strong collaboration with industry. She is currently supervising 5 PhD students and 10 MSc students.


Keywords: Logistics | Retail | OR & Management Science

Publicações

A Novel Three-Stage Short-Term Photovoltaic Prediction Approach Based on Neighborhood Component Analysis and ANN Optimized with PSO (NCA-PSO-ANN)

Power Systems | 2022

Ofori-Ntow Jnr

Y.Y. Ziggah

M. Rahmani-Andebili

M.J. Rodrigues

A roadmap for sustainability performance assessment in the context of Agri-Food Supply Chain

Sustainable Production and Consumption | 2022

S. Relvas

A novel two-phase decomposition-based algorithm to solve MINLP pipeline scheduling problem

Operational Research | 2022

S.A. MirHassani

S. Relvas

F. Hooshmand

A New Long-Term Photovoltaic Power Forecasting Model Based on Stacking Generalization Methodology

Natural Resources Research | 2022

Y.Y. Ziggah

M.J. Rodrigues

S. Relvas

A Novel Three-Stage Short-Term Photovoltaic Prediction Approach Based on Neighborhood Component Analysis and ANN Optimized with PSO (NCA-PSO-ANN)

Power Systems | 2022

Y.Y. Ziggah

M. Rahmani-Andebili

M.J. Rodrigues

S. Relvas