FAST-manufacturing

About the Project

Flexible And sustainable manufacturing

This project focus is on manufacturing flexibility and sustainability. The former is researched by optimizing scheduling operations in flexible manufacturing systems through the cumulative integration of the several sub-problems faced in manufacturing flexible systems. Sustainability is promoted via optimizing resource consumption and pollutant emissions along with the usual efficiency measures (makespan, tardiness, workload, etc.) for production operations scheduling problems. Regarding the emissions, it also accounts for the possibility of buying and/or selling pollution permits. Mathematical programming models are proposed, as well solutions methods to solve them. In addition, and due to the NP-hardness nature of the problems under consideration, solution approaches based on evolutionary algorithms that are capable of finding good solutions in a reasonable are also proposed.

Project datasheet

Acronym

FAST-manufacturing

Responsible

Dalila Fontes

Status

Concluded

Starting Date

January 1, 2018

Ending Date

January 30, 2022

Effective End Date

January 30, 2022

Global Budget

€226,481.96

Funding

€226,481.95

Website

Datasheet

Members

Team Leaders

Dalila Fontes

Research Coordinator

Team Members

Catarina Delgado

Dalila Fontes

Research Coordinator

Mahdi Homayouni

Dissemination

INESC TEC has a unique and differentiating management model, improved over its 35 years of history. Reflecting its unique position between academia and industry, the management at INESC TEC carefully balances, in a hybrid model, the academic culture of scientific freedom and dialogue with a culture of efficiency and responsibility in management.

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Associated Centres

Artificial Intelligence and Decision Support

Our Laboratory of Artificial Intelligence and Decision Support (LIAAD) conducts research in the fields of Artificial Intelligence, Machine Learning, Data Science, and Modelling. These areas are cross-cutting and apply to all sectors of society and the economy. The vast amounts of data being collected, alongside the ubiquity of digitalisation and sensorisation, are increasingly creating opportunities and challenges for automating decision support. The combination of Machine Learning and complex models is transforming the economy, healthcare, justice, industry, science, public administration, and education. This encourages us to invest in diverse technological and scientific approaches and perspectives. Our overarching strategy is to explore the flow and diversification of data, and to invest in research lines that will lead to the development of applied Artificial Intelligence foundations and models that are responsible and human centred.