Decision support methods for sustainable ship energy systems: A state-of-the-art review

Energy ◽  
2022 ◽  
Vol 239 ◽  
pp. 122288
Author(s):  
Nikoletta L. Trivyza ◽  
Athanasios Rentizelas ◽  
Gerasimos Theotokatos ◽  
Evangelos Boulougouris
Keyword(s):  
Decision Support ◽  
State Of The Art ◽  
Energy Systems ◽  
Support Methods

scholarly journals Multi-Criteria Decision Support Methods for Renewable Energy Systems on Islands

2015 ◽  
Vol 3 (3) ◽  
pp. 185-195 ◽  
Author(s):  
C. Wimmler ◽  
G. Hejazi ◽  
E. de Oliveira Fernandes ◽  
C. Moreira ◽  
S. Connors
Keyword(s):  
Renewable Energy ◽  
Decision Support ◽  
Energy Systems ◽  
Renewable Energy Systems ◽  
Support Methods

State of the art methods for combined water and energy systems optimisation in Kraft pulp mills

2021 ◽  
Author(s):  
Elvis Ahmetović ◽  
Zdravko Kravanja ◽  
Nidret Ibrić ◽  
Ignacio E. Grossmann ◽  
Luciana E. Savulescu
Keyword(s):  
Kraft Pulp ◽  
State Of The Art ◽  
Energy Systems ◽  
Pulp Mills ◽  
Art Methods ◽  
Kraft Pulp Mills

scholarly journals State-of-the-Art Prescriptive Criteria Weight Elicitation

2012 ◽  
Vol 2012 ◽  
pp. 1-24 ◽  
Author(s):  
Mona Riabacke ◽  
Mats Danielson ◽  
Love Ekenberg
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Theory ◽  
Decision Analysis ◽  
Analytical Methods ◽  
State Of The Art ◽  
Criteria Weights ◽  
Decision Making Processes ◽  
Method Of Assessment ◽  
Descriptive Theories

Comparatively few of the vast amounts of decision analytical methods suggested have been widely spread in actual practice. Some approaches have nevertheless been more successful in this respect than others. Quantitative decision making has moved from the study of decision theory founded on a single criterion towards decision support for more realistic decision-making situations with multiple, often conflicting, criteria. Furthermore, the identified gap between normative and descriptive theories seems to suggest a shift to more prescriptive approaches. However, when decision analysis applications are used to aid prescriptive decision-making processes, additional demands are put on these applications to adapt to the users and the context. In particular, the issue of weight elicitation is crucial. There are several techniques for deriving criteria weights from preference statements. This is a cognitively demanding task, subject to different biases, and the elicited values can be heavily dependent on the method of assessment. There have been a number of methods suggested for assessing criteria weights, but these methods have properties which impact their applicability in practice. This paper provides a survey of state-of-the-art weight elicitation methods in a prescriptive setting.

GUESS: projecting machine learning scores to well-calibrated probability estimates for clinical decision-making

2018 ◽  
Vol 35 (14) ◽  
pp. 2458-2465 ◽  
Author(s):  
Johanna Schwarz ◽  
Dominik Heider
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Clinical Decision Support ◽  
State Of The Art ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Calibration Method ◽  
Probability Estimates ◽  
Calibration Methods ◽  
Novel Method

Abstract Motivation Clinical decision support systems have been applied in numerous fields, ranging from cancer survival toward drug resistance prediction. Nevertheless, clinical decision support systems typically have a caveat: many of them are perceived as black-boxes by non-experts and, unfortunately, the obtained scores cannot usually be interpreted as class probability estimates. In probability-focused medical applications, it is not sufficient to perform well with regards to discrimination and, consequently, various calibration methods have been developed to enable probabilistic interpretation. The aims of this study were (i) to develop a tool for fast and comparative analysis of different calibration methods, (ii) to demonstrate their limitations for the use on clinical data and (iii) to introduce our novel method GUESS. Results We compared the performances of two different state-of-the-art calibration methods, namely histogram binning and Bayesian Binning in Quantiles, as well as our novel method GUESS on both, simulated and real-world datasets. GUESS demonstrated calibration performance comparable to the state-of-the-art methods and always retained accurate class discrimination. GUESS showed superior calibration performance in small datasets and therefore may be an optimal calibration method for typical clinical datasets. Moreover, we provide a framework (CalibratR) for R, which can be used to identify the most suitable calibration method for novel datasets in a timely and efficient manner. Using calibrated probability estimates instead of original classifier scores will contribute to the acceptance and dissemination of machine learning based classification models in cost-sensitive applications, such as clinical research. Availability and implementation GUESS as part of CalibratR can be downloaded at CRAN.

Sustainability Decision Support Framework for the Prioritization of Hydrogen Energy Systems

2017 ◽  
pp. 225-276
Author(s):  
Jingzheng Ren ◽  
Di Xu ◽  
Huan Cao ◽  
Shun’an Wei ◽  
Lichun Dong ◽  
...  
Keyword(s):  
Decision Support ◽  
Energy Systems ◽  
Hydrogen Energy ◽  
Decision Support Framework
Sign in / Sign up

Export Citation Format

Share Document