Outranking Method Research Articles

An appropriate artificial intelligence technique for plastic materials recycling using bipolar dual hesitant fuzzy set

Plastic recycling has become more important than ever as the globe struggles with growing environmental issues. This research explores the significant environmental impact of recycling plastic and its growing relevance. The pervasive material known as plastic presents a complex risk to both human health and ecosystems in contemporary life. It exacerbates problems including marine pollution, habitat damage, and wildlife entanglement because of its persistence in landfills and seas, which leads to serious ecological deterioration. In addition, producing plastic uses a lot of energy and produces a lot of greenhouse gas emissions, which exacerbate climate change. Through the use of multi-criteria decision making (MCDM), this study emphasizes how vital it is to support recycling activities in order to protect the environment and promote a sustainable future. The elimination and choice ex-pressing reality (ELECTRE) approach is used to rank the alternatives in this proposed research study that employs bipolar dual hesitant fuzzy sets (BDHFs). The most efficient and versatile outranking method for making decisions is the BDHF-ELECTRE approach. The weights of environment, economic, social, technical, and finally safety is computed using the entropy distance metric. The economic factor received the highest score of 0.2945 among the other factors since economic considerations are crucial in choosing the most efficient plastic recycling method, as they ensure sustainability, cost-effectiveness, resource allocation, and overall feasibility in managing plastic waste. The decision-makers determined that the mechanical recycling approach ought to be prioritized over all others for the efficient recycling of plastic waste. The robustness of the system is examined in the sensitive and comparative analyses. The proposed MCDM technique thus presents a viable solution, mitigating the adverse effects of plastic waste by conserving resources, reducing energy consumption, and curbing pollution.

A novel multicriteria web‐based decision support system to enhance resource allocation in energy companies: a portfolio selection with c‐optimal PROMETHEE

AbstractIn an uncertain world, decision support systems (DSSs) are essential for effective portfolio management. Electricity companies often face challenges in allocating limited resources among competing projects, influenced by sustainability and regulatory requirements. To address these challenges, multicriteria approaches have been developed, focusing on complex issues involving outranking methods. This paper introduces a methodological advance with a novel algorithm that enhances the c‐optimal PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations) approach, resolving scaling issues inherent in the PROMETHEE V method. Our key contribution is a refined procedure for efficiently identifying and comparing c‐optimal solutions while preserving the method's axiomatic integrity, ensuring the selection of the “true best” portfolio. Additionally, we present a web‐based DSS designed to implement this methodology, featuring interactive visualization and sensitivity analysis tools. Freely available online, both the algorithm and DSS were validated through a case study with a Brazilian electricity company, demonstrating their practical benefits and applicability across various business contexts.

CONTINUOUS PERFORMANCE EVALUATION OF EMPLOYEES USING AHP AND MODIFIED PUGH MATRIX METHOD: CONTRASTING WITH TOPSIS, PROMETHEE AND VIKOR

Applications of the AHP for employee performance evaluation in organizations are widely discussed in the literature. Contemporary organizations are increasingly discarding the traditional periodic appraisal systems and moving towards a real-time continuous process of evaluation. The existing multi-criteria decision making method (MCDM)-based employee performance evaluations are not suitable for such continuous evaluations, due to the complexity of the MCDM method. The current appraisal system is notoriously difficult to administer which prevents organizations from using it as an ongoing evaluation. There is a need for a simple yet robust multi-criteria decision making method for continuous performance evaluation of employees (CPEE). In this article, a modified version of the Pugh Matrix Method (MPMM) is proposed as a robust outranking method. The MPMM in combination with the AHP can function as an effective tool for CPEE. The MPMM is compared with other established and popular methods including TOPISIS, PROMETHEE and VIKOR. A statistical comparison using correlation validates the evaluation by the MPMM. There appears to be no significant difference in the evaluation of the MPMM with the other MCDM methods. Owing to its robustness and ease of use, the MPMM can easily be adopted by organizations for CPEE. The managerial implications and agenda for future research are also discussed.

Multicriteria decision support under uncertainty: combining outranking methods with Bayesian networks

AbstractAssessing alternative solutions that have uncertain evaluations in conflicting multiple criteria is not straightforward. Probabilistic models such as Bayesian networks (BNs) can effectively model and represent the uncertainty in such problems, but they do not include built-in mechanisms to guide different decision makers (DMs) with varying preferences toward the final decision. We propose a systematic approach to combine outranking methods with BNs to provide decision support for solutions with multiple and conflicting criteria under uncertainty. The proposed approach is based on Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE), and it offers different levels of precision and flexibility to the DMs in assessing the solutions. Our approach includes graphical tools and summary metrics to enhance the presentation of its results to the DMs. We test our approach with a case study on supplier selection where the uncertainty in supplier performances is modeled with a BN. We demonstrate that the proposed approach can enable the joint use of multiple criteria techniques with probabilistic modeling techniques like BNs to provide decision support in complex environments including uncertainty.

Land use suitability analysis using an approach combining GIS, regionalization, and PROMETHEE II

ABSTRACT Land-use Suitability Analysis, which aims to identify the suitability of a given area for a particular use by using a certain number of criteria, is a domain that has received a lot of attention from researchers during the last 20 years and most of the works that have been proposed rely on the use of multi-criteria methods which are based on a complete aggregation of alternatives such as Weighted Linear Combination (WLC). The outranking methods, which are multi-criteria methods that rely on a partial aggregation of alternatives, have rarely been used. In the context of Land-use Suitability Analysis, outranking methods quickly reach their computational limits because they require performing comparisons between each pair of pixels. To reduce the initial number of alternatives, we propose to proceed to the regionalization of the study area by grouping neighbouring pixels that have similar properties in homogeneous zones. We then use PROMETHEE II (Preference Ranking Organization Method for Enrichment of Evaluations), an outranking method whose objective is to rank a set of alternatives from the best to the worst. A study for the choice of a landfill site was carried out using our approach and the obtained results were compared with those of WLC.

Multiple criteria decision support system for customer segmentation using a sorting outranking method

For companies, customer segmentation plays a key role in improving supply chain management by implementing appropriate marketing strategies. The objectives of this research are to design and validate a multicriteria model to support decision making for customer segmentation in a business to business context. First, the model based on the transactional customer behaviour is extended by a hierarchy with three main criteria: Recency, Frequency and Monetary (RFM), customer collaboration and growth rates. Customer collaboration includes quota compliance, variety of products and customer commitment to sustainability (reverse logistics and shared information). Second, the Global Local Net Flow Sorting (GLNF sorting) algorithm is implemented and validated using real company data to classify 8,157 customers of a multinational healthcare company. Third, the SILS quality indicator has been implemented and validated to assess the quality of preference-ordered customer groups and its parameters have been adapted for contexts with thousands of alternatives. The results are also compared with an alternative model based on data mining (K-means). The multicriteria system proposed allows to segment thousands of customers in ordered categories by preferences according to company strategies. The segments generated are more homogeneous, robust and understandable by managers than those from alternative methods. These advantages represent a relevant contribution to automating supply chain management while providing detailed analysis tools for decision making.

Assessing Vulnerability to Health Impacts of Climate Change in French Municipalities

Abstract Background Climate change significantly threatens human health, particularly in vulnerable municipalities. Multiple climatic, environmental, and socioeconomic factors influence municipal vulnerability. To effectively reduce this vulnerable, evidence-based tools for local decision-makers are needed. This study aims to present the development and application of a multicriteria decision analysis (MCDA) model to assess vulnerability to the health impacts of climate change in French municipalities. Methods We selected 59 municipalities to develop and apply the model. The development of the MCDA model involved a literature review on climate change and health, as well as active participation from an interdisciplinary committee composed of members from academic, associative, and professional sectors. This led to identifying 38 indicators to characterize vulnerability based on three components: climatic exposure, environmental and socioeconomic sensitivity, and adaptive capacity. These indicators were used as input to the MCDA model, which applied an outranking method to rank municipalities from most to least vulnerable. Results The MCDA model generated partial rankings by the components of vulnerability, as well as a complete ranking that integrated the three components. The results revealed the main indicators that explained the rankings and allowed for a typology of municipalities. The results helped prioritize interventions by identifying the indicators that have the greatest impact on the vulnerability of each municipality studied. Conclusions This study highlights the importance of considering multiple criteria and their interdependencies when assessing vulnerability. The results of the MCDA model provide an evidence-based tool to support decision-making processes at the local level. The MCDA model is a valuable tool for identifying and prioritizing effective interventions to reduce vulnerability in the studied municipalities. Key messages • The developed MCDA model is a valuable tool for local decision-makers to prioritize interventions and reduce vulnerability. • The developed MCDA model provides a robust evidence-based tool to support decision-making processes.

An Aczel-Alsina aggregation-based outranking method for multiple attribute decision-making using single-valued neutrosophic numbers

The “single-valued neutrosophic set (SVNS)” is used to simulate scenarios with ambiguous, incomplete, or inaccurate information. In this article, with the aid of the Aczel-Alsina (AA) operations, we describe the aggregation operators (AOs) of SVNSs and how they work. AA t-norm (t-NM) and t-conorm (t-CNM) are first extended to single-valued neutrosophic (SVN) scenarios, and then we introduce several novel SVN operations, such as the AA sum, AA product, AA scalar multiplication, and AA exponentiation, by virtue of which we generate a few useful SVN AOs, for instance, the SVN AA weighted average (SVNAAWA) operator, SVN AA order weighted average (SVNAAOWA) operator, and SVN AA hybrid average (SVNAAHA) operator. Next, we create distinct features for such operators, group numerous exceptional cases together, and study the relationships between them. Following that, we created a way for “multiple attribute decision making (MADM)” in the SVN context using the SVNAAWA operator. We provided an illustration to substantiate the appropriateness and, additionally, the productiveness of the produced operators and strategy. Besides this, we contrasted the suggested strategy to the given procedures and conducted a comprehensive analysis of the new framework.

A Multi-Enterprise Quality Function Deployment Paradigm with Unstructured Decision-Making in Linguistic Contexts

This paper presents an operational framework of unstructured decision-making approach involving quality function deployment (QFD) in an uncertain linguistic context. Firstly, QFD is extended to the multi-enterprise paradigm in a real-world manufacturing environment. Secondly, hesitant fuzzy linguistic term sets (HFLTSs), which facilitate the management and handling of information equivocality, are designed to construct a house of quality (HoQ) in the product planning process. The technique of computing with words is applied to bridge the gap between mechanisms of the human brain and machine processes with fuzzy linguistic term sets. Thirdly, a multi-enterprise QFD pattern is formulated as an unstructured decision-making problem for alternative infrastructure project selection in a manufacturing organization. The inter-relationships of cooperative partners are directly matched with a back propagation neural network (BPNN) to construct the multi-enterprise manufacturing network. The resilience of the manufacturing organization is considered by formulating an outranking method on the basis of HFLTSs to decide on infrastructure project alternatives. Finally, a real-world example, namely, the prototype manufacturing of an automatic transmission for a vehicle, is provided to illustrate the effectiveness of the proposed decision-making approach.

Modeling assessment of groundwater vulnerability to contamination risk in a typical basement terrain using TOPSIS-entropy developed vulnerability data mining technique

This study involved a comparative analysis in the groundwater vulnerability domain, which is a crucial component of groundwater management decision support systems (DSS). This was achieved by creating models that covered the range of algorithms from the subjective to the data-driven. The study was conducted in a basement complex area. Databases of climatic, remote sensing, and geophysical datasets were created using varieties of data acquisition techniques. The datasets included in this assessment were: rainfall (R), land use (LU), bedrock topography (BT), recharge rate (Re), and slope (S). The slope and rainfall were determined to have the highest (0.78) and lowest (0.01) weighted factors, respectively, using the entropy method. For the development of the TOPSIS-Entropy model algorithm, the weights results were combined with the TOPSIS outranking method. To generate the Groundwater Vulnerability Model map of the study area, the hybrid model was applied to griddled raster layers of the factors. Also, the TOPSIS and Entropy-WLA model algorithms were also explored and used to generate groundwater vulnerability maps. The TOPSIS-Entropy algorithms produced an accuracy of 70%, while TOPSIS and Entropy-WLA produced accuracy of 50 and 47%, respectively. The resulting model maps were validated by using correlation technique on the produced map and the longitudinal conductance map of the study area. The TOPSIS-Entropy, which followed an object-oriented model pattern, demonstrates greater accuracy and has the potential to provide appropriate insights and alternatives to decision-making in the field of groundwater hydrology in the study area and other regions of the world with comparable geology.

A generalized linguistic gained and lost dominance score method for landslide hazard treatment

Reducing the risk of landslide hazards necessitates a prompt emergency decision regarding landslides, particularly when a hasty investigation lacks abundant data. Thus, the experience and opinions of experts, which are typically expressed in linguistic terms rather than exact values, can be utilized to the fullest extent to facilitate this process. Here I extend the gained and lost dominance score (GLDS) method to a generalized linguistic setting for modeling collective cognition in the selection of landslide treatment schemes for an open-pit mine. To do so, a generalized qualitative scale considering two cognitive bias arguments and one granularity parameter of linguistic term sets (LTS), which constitutes a generalized LTS that allows experts to express their evaluations in a flexible way, is presented to capture precise semantics of linguistic evaluations. Furthermore, several aggregation operators are given to deal with the generalized LTS for information fusion. To promote the application of the GLDS method within the context of multi-expert heterogeneous linguistic environment, I develop the framework of a generalized GLDS method by improving three aspects (i.e., the construction of dominance flow, the computing of lost dominance scores and the establishment of final aggregation function) of the classical one. Lastly, the effectiveness of the proposed method is examined by solving the multiple criteria decision-making problem we encountered as well as comparing with other outranking methods. The lattice anchored technique coupled with real-time monitoring is picked out to treat the landslide, which mitigates the production pressure in this high-slope open-pit mine.

Multi-criteria group decision-making for optimal management of water supply with fuzzy ELECTRE-based outranking method

This study elevates the potential of ELECTRE IV method using the logical backgrounds of fuzzy sets in the proposed fuzzy ELECTRE IV method to capture the water supply problem of Iran. ELECTRE IV method, being an advanced variant of ELECTRE family, operates on the outranking principle to achieve the results using three types of preferences and five dominance relations that exhibit different levels of superiorities among alternatives. The proposed procedure is designed to exhibit the linguistic preferences via triangular fuzzy numbers which are compared on the basis of their corresponding magnitudes. The proposed approach successfully captures the pseudo criteria using three threshold values to filter out the preferences and then classifying the level of dominance. Moreover, ranking is derived by combining two pre-orders, i.e., ascending and descending pre-orders. The main contribution of this study is the implementation of outranking caliber of proposed fuzzy ELECTRE IV method to address the water supply problem in the Kermanshah province of Iran, concerning the selection of optimal site for Kandoleh dam. The comparisons with two existing techniques, fuzzy TOPSIS method and integrated fuzzy delphi and fuzzy ELECTRE method, are conducted to verify the authenticity and competency of the proposed technique.

Ranking food products based on estimating and combining their microbiological, chemical and nutritional risks: Method and application to Ready-To-Eat dishes sold in France

Microbiological (M), chemical (C), and nutritional (N) risks associated with food products are usually assessed and managed independently by experts in public services or food companies. This can render difficult the comparison of food products in term of overall risk for the consumer. The objective of this study was to suggest a relatively simple method to (i) classify food products based on their M, C and N risks, and (ii) aggregate these risks and rank the food products accordingly. The method was developed and applied to 17 ready-to-eat (RTE) dishes available on the French market. With regard to food safety, the individual M and C risks were characterized considering likelihood and severity as recommended by the Codex Alimentarius. With regard to nutrition/health, the N risk was estimated based on the tendency of the dish to contribute to nutrient adequacy and to a healthy eating pattern. Finally, the outranking method PROMETHEE was applied to aggregate the three M, C, N risks and rank the food dishes. Food products were ranked relatively to each other, not in absolute terms. When we attributed the same weight to M, C and N risks, the RTE dish “Duck Parmentier” had the highest risk score while “Papillote of chicken, potatoes and small vegetables” and “Vegetarian plate vegetables and quinoa” had the lowest. However, this overall ranking changed according to the weight assigned to individual M, C and N risks, at least for food products whose scores varied according to risk types, such as”sushi discovery” (high M and C risks, low N risk). Since the risk ranking method developed here was built with assumptions and hypotheses related to the specific case study, more applications are needed to assess whether it can be generic. Nevertheless, this method is well grounded, objective, transparent, relatively fast and easy to set up. It might lead to further development of decision tools, particularly for consumers. This study paves the way towards food product multi-risk ranking.

A parametric distance-based outranking method for probabilistic linguistic multi-criteria decision-making problems

A parametric distance-based outranking method for probabilistic linguistic multi-criteria decision-making problems

Development of the ELECTRE Method Under Pythagorean Fuzzy Sets Based on Existing Correlation Coefficients for Cotton Fabric Selection

ABSTRACT Cotton fabric selection is a challenging task in the garment product design and development process, and the selection of optimal alternative under the presence of multiple decision criteria becomes complex, and hence it is considered as a multi-criteria decision-making (MCDM) problem. In addition, the selection process involves fuzziness and uncertainty. In this study, Pythagorean fuzzy sets (PFSs) are introduced to handle uncertain information. Elimination and choice translating reality (ELECTRE) is a well-known outranking method for solving MCDM problems. Therefore, we extend the ELECTRE method under the PFS environment, and a correlation-based closeness coefficient is proposed to compare Pythagorean fuzzy numbers (PFNs). This paper applies the proposed PF-ELECTRE approach in solving a practical case involving the ranking cotton fabrics. To exhibit the superiority and robustness of the suggested method, sensitivity analysis is performed to examine the impacts of weights variation, as well as a comparative analysis is carried out between the PF-ELECTRE with several existing MCDM methods. The research contributes to the advancement and development of outranking MCDM methods through a novel PF-ELECTRE approach that utilizes the weighted correlation coefficient. Moreover, the developed method can obtain reliable results and can be used to other textile domains.

An interval-valued neutrosophic based MAIRCA method for sustainable material selection

A plethora of Multi-Criteria Decision-Making (MCDM) methods is available for selecting an optimal material for a design problem with quantitative measurements. However, little research has been conducted on material selection methods in an interval neutrosophic environment to address uncertainty. In this research paper, a unique framework is presented that combines Interval-Valued Neutrosophic Sets (IVNSs) with the entropy–MultiAtributive Ideal-Real Comparative Analysis (MAIRCA) framework with the aim of simultaneously handling the objective criteria with crisp inputs and subjective criteria with ambiguous or uncertain information. To demonstrate the applicability of the proposed approach, a wing-spar of a Human-Powered Aircraft (HPA) is considered. At first, a panel of three subject experts makes judgments on the alternatives or criteria linguistically through IVNSs. Next, the skill level of each expert involved in the decision-making process is assessed using a novel weighting approach with linguistic information. After obtaining the objective criteria weights using the entropy weighting method, the performance of candidate materials for the HPA spar is evaluated using the MAIRCA method. The results indicate that S-glass70%-Epoxy Continuous Fibers, Ti-2Fe-3Al-10V and Al-7075 T6 are the top-ranked alternatives for spar application. The strong correlation of the proposed approach with other extant MCDM techniques demonstrates the reliability of the results obtained. In addition, the efficacy and practicality of the material selection tool is also validated with an outranking method in a simplified neutrosophic environment.

A Novel Axial-Distance-Based Aggregated Measurement (ADAM) Method for the Evaluation of Agri-Food Circular-Economy-Based Business Models

Multicriteria decision making (MCDM) is a field that helps decision makers evaluate alternatives based on multiple criteria and encompasses scoring, distance-based, pairwise comparison, and outranking methods. Recent developments have aimed to solve specific problems and overcoming the limitations of previous methods. This paper proposes a new axial-distance-based aggregated measurement (ADAM) method, which is used in combination with the best-worst method (BWM) to evaluate agri-food circular economy (CE)-based business models (BMs) to create a more sustainable and efficient system for producing and consuming food. This paper proposes nine BMs, which were evaluated against eight criteria. The BWM method was used to obtain the criteria weights, while the ADAM method was used to obtain a final ranking of the BMs. The results indicate that a sustainable circular agri-food supply chain is a BM that can bring companies the most significant progress in business and strengthen their position in the market. We concluded that the ADAM method is effective for solving MCDM problems and that, overall, the model is an effective tool for solving the problem defined in this study. The main contributions are the development of a new MCDM method and a hybrid model, the establishment of the framework for evaluation and selection of CE-based BMs, and the identification of the most important ones.

Extended QUALIFLEX method for electronic music acoustic quality evaluation based on the picture fuzzy multiple attribute group decision making

In the past, different useful extensions of fuzzy sets were established by the researchers to manage the vagueness and uncertainty in various practical problems. Usually, the real numbers are utilized to express the decision information, but it is noted that the description of attributes using picture fuzzy sets (PFSs) proves to be more appropriate. As a powerful decision tool, PFSs provides more decision information that requires the application of some specific situations more types of response of human ideas: yes, contain, no, reject. QUALIFLEX (qualitative flexible multiple criteria method), is one of the well-known outranking methods to solve the multiple attribute group decision making (MAGDM) problems with crisp numbers. The QUALIFLEX method can perfectly address the complex MAGDM problems where a lot of attributes are utilized to assess a limited number of alternatives. The electronic music acoustic quality evaluation is a classical MAGDM. This paper proposes and utilizes the QUALIFLEX to develop the picture fuzzy QUALIFLEX(PF-QUALIFLEX) method for MAGDM. The current study is mainly devoted to explore and extend the measurement of alternatives and ranking according to the QUALIFLEX under the background of PFSs. Furthermore, an example to evaluate the electronic music acoustic quality is handled through the proposed method to substantiate the extended approach.

Prometheeγ: A new Promethee based method for partial ranking based on valued coalitions of monocriterion net flow scores

AbstractMulticriteria decision aid consists of helping decision makers to compare (rank, choose, sort, etc.) different alternatives which are evaluated on conflicting criteria. In the last decades, numerous decision aid methods have been developed. Three main categories of decision aid methods are usually considered: the aggregating, interactive and outranking methods. While aggregation methods produce a complete ranking of the set of alternatives, outranking methods usually allow some pairs of alternatives to remain incomparable. This happens either if the two alternatives present some strong conflicting information or if there are not enough elements in the decision problem to state the preference or indifference between them. A well‐known family of outranking procedures are the Promethee methods. The aim of this work is first to provide an analysis of the incomparability relation produced by Promethee I. From our point of view, some shortcomings of this incomparability relation are presented. Then, a new method based on the comparison of weighted coalitions of mono‐criterion net flow score differences, called Promethee , is proposed. Promethee and Promethee i are then further compared.

An Improved ELECTRE II-Based Outranking Method for MADM with Double Hierarchy Hesitant Fuzzy Linguistic Sets and Its Application to Emergency Logistics Provider Selection

An Improved ELECTRE II-Based Outranking Method for MADM with Double Hierarchy Hesitant Fuzzy Linguistic Sets and Its Application to Emergency Logistics Provider Selection