Using the TODIM-FSE method as a decision-making support methodology for oil spill response
Introduction
Fuzzy Synthetic Evaluation (henceforth FSE) was first launched as an environmental index to evaluate the water quality in reservoirs [1]. In the same article, both the need to take into account several conflicting elements simultaneously, as well as the inaccurate judgments of these elements, were clearly underlined. This is why concepts of multi-criteria decision analysis, together with fuzzy logic were brought in.
Several papers have been published using the same methodology. Most of these have adjusted environmental modeling in order to create alternative evaluation indices [1], [2], [3]. However, articles using the FSE in well-defined decision problems [4], [5] may also be found. For this reason, we consider FSE as a multi-criteria decision method.
Another multi-criteria method already known in the scientific literature is TODIM [6], [7], [8], [9], [10]. Its main feature is to take into account the risk embedded in the decision makers´ judgments, by adapting in its aggregation function the value function of Kahneman and Tversky´s Prospect Theory [11]. However, different from FSE, TODIM is a method that analyzes a set of alternatives (or courses of action) and provides their order of preference.
The main objective of the present text is to introduce an innovative method for solving multi-criteria classification problems (Pβ) that merges characteristics from both methods (TODIM and FSE), and also demonstrate its characteristics, as well as describe the procedures aimed at obtaining final results. In order to illustrate these proposals for using this method (henceforth TODIM-FSE), a model is provided which has been applied in a case study. The aim had been to establish the most suitable contingency plan for each oil spill occurrence. A further research objective is to describe the context in which the model is applied. This is, namely, the development of SISNOLEO (an acronym in Portuguese for an Information System for Oil Spill Planning and Response). This paper will also provide a general description of the aforementioned system, in which the model was seen to be embedded.
Being able to act quickly in emergency situations, whether brought on by nature or not, is a clear concern and the literature abounds with proposed solutions. In this sense, [12] proposes an expert system for fast disaster assessment integrating fuzzy logic, Delphi method and several MCDM methods, while [13] develops a procedure to simplify the consistency test used in Analytical Network Process (ANP). As a result, it is possible to improve the efficiency of response decision making in risk assessment. In [14], a new expert system for disaster diagnosis is described and, in [15], a model was built in order to establish a rapid risk assessment applied to the tourism industry. Analyzing earthquake disasters as typical giant complex systems, [16] presents the meta-synthesis assessment framework of these systems. In order to analyze earthquake situations, [17] presents an intelligent simulation system based on a development platform of a geographic information system and artificial intelligence. This previous paper intends to identify the weaknesses of the structure and infrastructure system in pre-earthquake conditions, quickly assess earthquake damages and respond in a fast and intelligent way to the public and the government. Meanwhile, [18] proposes a model to evaluate the relative severity of earthquakes in different regions of China.
In oil spill situations, several concerns must be taken into account simultaneously. One of them is the fate of the oil in a marine environment, as studied in [19], [20]. The shoreline ecosystems sensitivity should also be considered in case of oil spill, as suggested by [21]. The model proposed in this article considers these last two aspects.
Section snippets
The TODIM-FSE method: A Pβ approach
The TODIM-FSE method assembles characteristics from two different multi-criteria methods. The fundamental idea of the FSE aggregation procedure is to derive a weighted sum of the membership values for each category. These weights relate to the relative importance of criteria. This should be carried out successively until a final vector is obtained. The components of this vector are the membership values for each alternative related to the defined categories.
To illustrate the general algebra for
Application context: The Brazilian oil spill information system (SISNOLEO)
The Brazilian contingency structure of response for oil spill is still under development. The Brazilian National Contingency Plan, for example, is being evaluated by national authorities and it is still awaiting government approval in a legal procedure which began in 2002.
One positive element in terms of the draft of the Brazilian National Contingency Plan is the SISNOLEO. This has been defined as an information system with real time access capable of collecting, analyzing, providing and
Discussion and conclusion
This paper provides an alternative multi-criteria sorting method, to support the construction of decision models. The structure and procedures for application are fully described. A case study have attempted to illustrate how the constructed model may be put into practice. It is also possible to compare TODIM-FSE with alternative multi-criteria decision aid classification methods available. One well-known sorting approach is the ELETRE TRI, described by Brito et al. [33] and Dias et al. [34].
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2020, Expert Systems with ApplicationsCitation Excerpt :TODIM method (an acronym in Portuguese ”TOmada de Decisão Iterativa Multicritério”), proposed by Gomes and Lima (1992), is a valuable and well-known multi-criteria decision making (MCDM) method based on prospect theory (Kahneman & Tversky, 1979; Tversky & Kahneman, 1992) that considers human’s psychological behavior under risk and uncertainty, which has been widely used to solve different decision making problems (Fan, Zhang, Chen, Liu, 2013; Gomes & Lima, 1992; Gomes & Rangel, 2009) To tackle real world complex decision problems under uncertainty, TODIM method has been extended to fuzzy environment, in which extant fuzzy TODIM methods can manage various types of information to model the uncertainty, such as interval numbers (Hu, Yang, & Chen, 2018; Jiang, Liang, & Liang, 2017), linguistic information (Huang, Li, & Liu, 2017; Passos, Teixeira, Garcia, Cardoso, & Gomes, 2014; Tosun & Akyüz, 2015; Tseng, Lin, Tan, Chen, & H., 2014), linguistic Z-numbers (Wang, Cao, & Zhang, 2017), intuitionistic fuzzy numbers (Krohling, Pacheco, & Siviero, 2013; Li, Wu, Zhang et al., 2015; Mohagheghi, Mousavi, Aghamohagheghi, & Vahdani, 2017), interval intuitionistic fuzzy numbers (Li, Shan, & Liu, 2015), triangular intuitionistic fuzzy numbers (Qin, Liang, Li, Chen et al., 2017), intuitionistic uncertain linguistic information (Wang & Liu, 2017), 2-dimension uncertain linguistic information (Liu & Teng, 2015; 2016), interval type-2 fuzzy information (Qin, Liu, & Pedrycz, 2017; Sang & Liu, 2016), hesitant fuzzy information (Tan, Jiang, & Chen, 2015; Zhang & Xu, 2014), hesitant fuzzy linguistic information (Li, Chen, Qu, & Xu, 2018; Wang, Wang, & Zhang, 2016; Wang, Wu, Wang, Zhang, & Chen, 2016; Wei, Ren, & Rodríguez, 2015; Zhou, Wang, & Zhang, 2018), unbalanced hesitant fuzzy linguistic information (Yu, Zhang, Zhong, & Sun, 2017), neutrosophic numbers (Ji, Zhang, & Wang, 2017; Zhang, Liu, & Shi, 2016), pythagorean fuzzy information (Geng, Liu, Teng, & Liuet al., 2017; Ren, Xu, & Gou, 2016), heterogeneous modelling (Fan, Zhang, Chen, Liu, 2013; Lourenzutti, Krohling, & Reformat, 2017; Wang & Li, 2015; Wang, Labella, Rodríguez, Wang, & Martínez, 2017) and so forth. Existing fuzzy TODIM methods (Hu et al., 2018; Huang et al., 2017; Li, Wu et al., 2015; Wang, Labella et al., 2017; Wei et al., 2015; Yu et al., 2017) consider the uncertain information from various perspectives and have achieved fruitful results, which not only enrich the theoretical methods of fuzzy TODIM, but also make significant contributions to current fuzzy MCDM problems.
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