DECISING – DECISion science and management engineerING | CEGIST

About

Driven by real world challenges on the frontiers of engineering and management, and grounded on the principles of decision-making and decision-aiding theory, the Decision Science and Management Engineering (DECISING) branch of CEG-IST is a multimethodological area that develops methods, models, decision-support tools and knowledge to assist engineering management practice in complex contexts that involve diverse stakeholders, multiple objectives and several sources of uncertainty. At DECISING we follow a collaborative-learning constructivist paradigm integrating socio-technical processes to design sustainable solutions to problems of performance appraisal and value measurement, evaluation and prioritization, resource allocation and budgeting, risk analysis, project management, policy design and strategic choice. Our mission is driven by industry innovation, multidisciplinary research and spin-off partnerships, and is currently focused on (although not limited to) health, energy, and environmental settings, always exploring cross-fertilization with the other areas of CEG-IST and international collaboration.

Group Leader

Mónica Duarte Oliveira

Integrated member

Full Professor

Selected Publications

Enhancing optimization planning models for health human resources management with foresight

Omega | 2020

M. Amorim-Lopes

M. Oliveira

M. Raposo

T. Cardoso-Grilo

António Alvarenga

M. Barbas

M. Alves

A. Vieira

A. Barbosa-Póvoa

MACBETH

C.A. Bana E Costa

J.-M. De Corte

J.-C. Vansnick

Modelling Multicriteria Value Interactions with Reasoning Maps

European Journal of Operational Research | 2016

Teresa C. Rodrigues

Gilberto Montibeller

Mónica Oliveira

Carlos A. Bana e Costa

Multi-criteria decision analysis for health technology assessment: addressing methodological challenges to improve the state of the art

The European Journal of Health Economics | 2019

Mónica Oliveira

Inês Mataloto

Panos Kanavos

PROBE - A multicriteria decision support system for portfolio robustness evaluation

J.C. Lourenço

A. Morton

C.A. Bana E Costa