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Development of a Decision Support Tool for Sustainability Assessment of Energy Recovery Systems Using Refuse Derived Fuel
Published in Naim Hamdia Afgan, Maria da Graça Carvalho, New and Renewable Energy Technologies for Sustainable Development, 2020
D. Kolaitis, D. Giannopoulos, M. Founti
In the present work a decision-supporting tool has been developed, which uses a variation of the well-known PROMETHEE multi-criteria method. The outcome of the computational procedure is the positive and negative “flow” of each alternative that is being compared. The relative values of these flows determine the final ranking of each proposed solution. The methodology has been applied to evaluate four different energy recovery technologies used for Refuse Derived Fuel (RDF). The studied assessment problem has a clear multi-dimensional character, since it can be seen from an environmental, social, financial and/or technical point of view. Consequently, only a multiple criteria method is capable of reflecting this inherent complex character. The problem has been seen from four different aspect-scenarios, which correspond to four decision-maker “archetypes”.
Model Application
Published in Igor Linkov, Emily Moberg, Benjamin D. Trump, Boris Yatsalo, Jeffrey M. Keisler, Multi-Criteria Decision Analysis, 2020
Igor Linkov, Emily Moberg, Benjamin D. Trump, Boris Yatsalo, Jeffrey M. Keisler
As explained above, the PROMETHEE system works by comparing both across criteria and within a criterion (across alternatives). Additional information is used to parameterize the problem and to serve as a mathematical tie-breaker when there is no clearly dominant winner (i.e. one alternative does not score best across all criteria, as is rarely the case). This additional information comes in the form of preference and indifference thresholds (Brans and Mareschal 2005). This can be conceptualized by considering the example of tuning a guitar. The tuner plays a note. When we pluck the guitar string at the same note, an untrained listener may hear no difference until 1/4 of a note higher, at which point he tentatively says, “Well, maybe I heard a difference?” We would consider this the indifference point, as any difference smaller than that is insignificant. If we continue to play increasingly out of pitch, our listener will eventually insist that he heard a difference, and any difference larger than that he is sure to identify; this is analogous to the preference threshold. However, these thresholds could be different for a trained violinist versus someone who is tone-deaf, which is an important aspect to these parameters. The way in which transitions occur can also vary. We can imagine cases in which our listener is surer of a difference with every turn of the tuning peg or a case in which our listener is unsure up until a half-note’s difference, at which point they are very sure.
A Robust Approach for Course of Action Comparison and Selection in Operation Planning Process
Published in Sarah Ben Amor, Adiel Teixeira de Almeida, João Luís de Miranda, Emel Aktas, Advanced Studies in Multi-Criteria Decision Making, 2019
Ahmet Kandakoglu, Sarah Ben Amor
PROMETHEE is a well-known family of outranking methods in MCDA developed by Brans and Vincke (1985) and further improved by Brans et al. (1986). It aggregates valued preference relations of every alternative pairs on each criterion to calculate the flow scores (entering, leaving, and net scores) and then provides a complete or partial ranking of alternatives based on these scores. Although PROMETHEE I builds a partial ranking of alternatives using the entering and leaving flows, PROMETHEE II provides a complete ranking of alternatives by considering the net flows. It is a rather simple and practical method that has been used in many real-world problems (Behzadian et al., 2010; Brans and De Smet, 2016). To apply the PROMETHEE method, the evaluations of the alternatives with respect to the criteria, the preference information for the criteria weights, indifference, and preference thresholds should be provided. Obviously, each set of these parameters may result in different rankings of alternatives.
A proposed framework for multi-tier supplier performance in sustainable supply chains
Published in International Journal of Production Research, 2023
Yigit Kazançoglu, Yucel Ozturkoglu, Sachin Kumar Mangla, Melisa Ozbiltekin-Pala, Alessio Ishizaka
Jean-Pierre Brans developed the MCDM method PROMETHEE in the 1980s (Brans and Vincke 1985). The most important advantage of PROMETHEE is that it is easy to use (Elevli, 2014; Mladineo, 2016). Moreover, the distinctive point of the method is that it does not require normalisation, and different types of functions can be used for each evaluation criterion in paired comparisons (Wu and Abdulnour, 2020). This method can be used in many areas, including logistics problems, SCM, banking, workforce planning, investment decisions, healthcare, human resources (Ishizaka and Pereira 2016; Lai and Ishizaka 2020), higher education (Ishizaka, Resce, and Mareschal 2018; Ishizaka et al. 2020), school systems (Ishizaka and Resce 2020), tourism (Lolli et al. 2019; Nieto-Garcia et al. 2019), culture (Collins, Ishizaka, and Snowball 2019), waste treatment (Lolli et al. 2016), and the pharmaceutical and chemical industries.
Modelling participation in road accidents of drivers with disabilities who use hand controls
Published in Journal of Transportation Safety & Security, 2023
Đorđe Petrović, Dalibor Pešić, Radomir M. Mijailović, Bojana Milošević
A large number of experts defined a large number of significant predictors. А procedure for reducing the number of significant predictors was defined to create more practical models. In this paper, the PROMETHEE II method of multicriteria decision-making was used to filter the predictors. PROMETHEE (The Preference Ranking Organisation METHod for Enrichment of Evaluations) analyses the interrelationship of alternatives by expressing their dominance over others and the dominance of others over them (Brans & De Smet, 2016). The PROMETHEE II method gives a unique value representing the difference between the dominance of the alternative over other alternatives and the dominance of other alternatives over them (Brans & De Smet, 2016). This value ranges from 1, which shows that the predictor is dominant over all others, to −1, which shows that all others are dominant over the predictor. The value 0.5 was adopted as the limit value, and the adopted preference function was linear. All predictors with dominance over this value were adopted in further analysis and were considered as dominant predictors.
A bi-objective mixed-model assembly line sequencing problem considering customer satisfaction and customer buying behaviour
Published in Engineering Optimization, 2018
Masoud Rabbani, Razieh Heidari, Hamed Farrokhi-Asl
Given that the fourth factor (i.e. innovation in an order) is a qualitative factor, the value of this variable for a particular industry is determined by gathering comments from experts who are active in the field. After calculating the amount of these variables and determining the weight of each factor based on experts’ views, it is possible to prioritize the received orders. PROMETHEE, as an effective method for multi-criteria decision analysis, is applied for this purpose. PROMETHEE can be described in five main stages: Determine the value of the preference function for all pairs of decision-making units (DMUs) in each criterion.Designate individual preference degree for all pairs of DMUs in each criterion (normalization of value of the preference function).Designate the multi-criteria preference degree for all pairs of DMUs.Determine multi-criteria preference flow (outputs, inputs and net) for each DMU.Determine the ranking of DMUs based on net flow.