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Preference Ranking Organization METHods for Enrichment Evaluations (PROMETHEE), based on outranking relation theory, are used extensively in Multi-Criteria Decision Aid (MCDA). In PROMETHEE, an overall preference index based on weighted average aggregation represents the intensity of preference for one pattern over another pattern and can be measured by a given preference function. Unfortunately, as the criteria making up the patterns are not always independent, the assumption of additivity among single-criterion preference indices may not be reasonable. This paper develops a novel PROMETHEE-based perceptron using nonadditive preference indices for ordinal sorting problems. The applicability of the proposed non-additive PROMETHEE-based single-layer perceptron (SLP) to bankruptcy prediction is examined by using a sample of 53 publicly traded, Taiwanese firms that encountered financial failure between 2000 and 2008. The proposed model performs well compared to PROMETHEE with additive preference indices and other additive PROMETHEE-based classification approaches.

Classification algorithm selection is an important issue in many disciplines. Since it normally involves more than one criterion, the task of algorithm selection can be modeled as multiple criteria decision making (MCDM) problems. Different MCDM methods evaluate classifiers from different aspects and thus they may produce divergent rankings of classifiers. The goal of this paper is to propose an approach to resolve disagreements among MCDM methods based on Spearman's rank correlation coefficient. Five MCDM methods are examined using 17 classification algorithms and 10 performance criteria over 11 public-domain binary classification datasets in the experimental study. The rankings of classifiers are quite different at first. After applying the proposed approach, the differences among MCDM rankings are largely reduced. The experimental results prove that the proposed approach can resolve conflicting MCDM rankings and reach an agreement among different MCDM methods.

Hesitant fuzzy sets (HFSs), an extension of fuzzy sets, are considered to be useful in solving decision making problems where decision makers are unable to choose between several values when expressing their preferences. The purpose of this paper is to develop two hesitant fuzzy multi-criteria decision making (MCDM) methods based on prospect theory (PT). First, the novel component-wise ordering method for two hesitant fuzzy numbers (HFNs) is defined; however, this method does not consider the length of the two HFNs. Second, by utilizing the directed Hausdorff distance between two imprecise point sets, the generalized hesitant Hausdorff distance is developed, which overcomes the shortcomings of the existing distance measures. Third, based on the proposed comparison method and distance, as well as PT, the extended TODIM and Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) approaches are developed in order to solve MCDM problems with hesitant fuzzy information. Finally, a practical example is provided to illustrate the pragmatism and effectiveness of the proposed approaches. Sensitivity and comparison analyses are also conducted using the same example. The findings indicate that the proposed methods do not require complicated computation procedures, yet still yield a reasonable and credible solution.

Since past two decades, logistics outsourcing has got immense importance in the context of supply chain management. Logistics outsourcing or outsourcing of third party logistics (3PL) comprises involvement of outside firms to execute logistics activities of a firm efficiently. As deployment of 3PL providers has gained huge momentum in recent times, appropriate selection of 3PL providers seems indeed a necessity. The present work intends to propose a decision support framework for evaluation and selection of 3PL providers in pursuit of fulfilling various business needs. A decision support framework based on dominance measure concept integrated with grey set theory has been projected herein. Result obtained thereof has been compared to that of grey-TOPSIS. A case empirical research has also been reported. The proposed dominance-based decision support system in light of grey set theory has been conceptualized and is basically a simplified version of TODIM and PROMETHEE. It explores dominance between two alternatives with respect to a particular criterion, based on which a global dominance measure is computed to facilitate deriving ranking order of candidate 3PL providers. The proposed approach delineated in this research seems straightforward, easy to execute and can exclude complex and tedious computational steps of TODIM as well as PROMETHEE.

Being a decision-making process, software evaluation and selection is complicated, time-consuming, costly and also critical to the success of the project/work/organization. In this study, evaluation and selection of customized software which are used to identify the physical effect (explosion, fire and toxic emission) distances for industrial establishments containing, using or storing hazardous chemicals has been considered. These software are called the “Hazard Analysis and Consequence Modeling” (HACM) software in the literature. This study first prepared Likert scale-based questionnaires which were distributed to Environmental Health and Safety (EHS) professionals to obtain their opinions. Likert scale was used with Analytic Hierarchy Process (AHP) method in the processing of data obtained from questionnaires in order to get over the inconsistency problem of pairwise comparison matrices. The Likert scale-based AHP method was used to determine the weights of the criteria, and Preference Ranking Organization METHod for Enrichment Evaluations (PROMETHEE) method was used to obtain the final ranking. PROMETHEE method was preferred for having the opportunity of evaluating the binary “Yes–No” questions in the solution process. Finally, application results were given to illustrate the proposed method by using the PROMETHEE software “Visual PROMETHEE 1.4.0.0”.

Decision making remains a prominent issue in all the problem domains. To make better decisions, multiple factors of the given problem need to be considered and evaluated. Multi-criteria decision-making methods have been used popularly for solving decision-making problems characterized by multiple factors. When multiple factors are considered, it is recommended to categorize the factors into the main criteria and sub-criteria. In this paper, GRAP-an integrated ranking algorithm has been developed by combining Grey Relational Analysis, Rank Sum, and Preference Ranking Organization Method Enrichment Evaluation methods (PROMETHEE) to solve decision-making problems. The weights of the sub-criteria are calculated using the Rank Sum method. Grey Relational Analysis method is used to convert the sub-criteria values into main criteria values in the form of evaluation scores of alternatives. The final ranking scores of the alternatives are obtained using the PROMETHEE method. A decision model is developed using the proposed GRAP algorithm and applied to the Job Profile selection case study. The developed decision model showed much better results compared to other MCDM approaches namely the Simple Additive Weight method, TOPSIS, VIKOR, and Complex Proportional Assessment (COPRAS). Further, a sanity check has been carried out by comparing the results of the decision model with experts’ opinions.

E-commerce has grown considerably with increasing access to the Internet. As a means of e-commerce, the virtual brand community (VBC) has become one of the important channels for brand enterprises to promote products, enhance brand awareness and compete with enterprises in the digital economy. The most important task of any website is the provision of tailored information and services to satisfy client needs. This research aimed to evaluate the status of websites and services provided by Chinese mobile internet enterprises. Based on the Fogg behaviour model, initial criteria and sub-criteria were constructed, and a new hybrid multiple criteria decision-making (MCDM) model was used in this study that combines the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique and the analytic network process (ANP) method to determine the interaction between factors and to determine the weight of each factor. Finally, the PROMETHEE method was utilised to rank and develop the consumer interaction capability of the VBC of Chinese mobile internet enterprises. Once these perceptions were captured, among the three criteria determined for the evaluation of the VBC of different mobile internet enterprises, affective involvement was the most important criterion, followed by motivation and environment triggers. Moreover, the VBCs of three communication enterprises were evaluated and ranked to validate the proposed model.

The development of water storage schemes in Sub-Saharan Africa (SSA) is considered a major aid for those regions with unequal water distribution, limited accessibility and anticipated climate change impacts. Great attention is given by many SSA countries to set up different water storage schemes that may improve rural and urban development on a national level. The funding for the water storage schemes is often derived from foreign agencies which conduct feasibility studies for the financing of potential investments. Often however, the feasibility studies rely on a single monetary criterion which may not identify the most appropriate water storage in each case. In addition, limited data availability in many SSA countries increases the difficulty of identifying the most suitable storage option. This paper develops a multicriteria framework for the integrated evaluation of water storage strategies in Sub-Saharan African countries. A set of economic, agronomic and opinion-based criteria are assessed through the PROMETHEE II outranking approach. The introduction of crop modeling complements the limited field data available in agronomic criteria and enhances the scientific rigor of the method. Ethiopia is adopted as a representative case of SSA countries where a diverse set of water storage options is currently under construction, often financed by foreign agencies.

Production in small and medium enterprises (SMEs) makes a substantial contribution to the Gross Domestic Product directly and indirectly in developing economies including India. In the present time, applying Industry 4.0 to the SMEs will build a smart manufacturing system that will prove to be economically feasible as well as socially sustainable. The purpose of this study is to identify and prioritize major barriers of implementing Industry 4.0 in Indian SMEs. A questionnaire with 12 barriers which were identified based on the literature survey and expert discussion was made to be filled by industry experts of production, information technology, business and members of the top management in SMEs. Further, Multi-Criteria Decision Making (MCDM) methods like TOPSIS, VIKOR and PROMETHEE are used to find the rank for each barrier. The study reveals that the major implementation barriers of Industry4.0 in Indian SMEs are fear of unemployment, lack of IT training, poor IT infrastructure, etc. The ranking for each barrier will not only help to assess risks in manufacturing, supply chain or business initiative, but also to help the managers in devising risk mitigation plans. This study may be used by firms working under the manufacturing sector.

As we search to achieve sustainable development and substance, energy security and efficiency should be viewed not only from the perspective of addressing short-term challenges, but also as a necessity for long-term growth of the economy. Latterly, some articles have majored on selecting the best energy policy and arbitrating the best energy alternatives. In this paper, AHP, VIKOR, PROMETHEE and TOPSIS approaches are suggested for the selection among energy policies. The methodology is based on these four methods under a comparison. In the application of the proposed methodologies, the best energy policy is determined for Turkey.

The Preference Ranking Organisation Method for Enrichment Evaluations (PROMETHEE) method offers a way to undertake the ranking of a finite set of decision alternatives based on values from a number of criteria. As an outranking method it is a multi-criteria decision aid in the ranking process. This chapter outlines the rudiments of the PROMETHEE method, and applies it in a tutorial problem, including the presentation of example numerical calculations. The inclusion of an uncertainity based analysis with PROMETHEE in the presented text demonstrates the developmental potential of PROMETHEE. The problem considered throughout the chapter concerns the relative performance ranking of individual police forces in a region of the UK, based on their levels of sanction detections (clean-up rates).

The Preference Ranking Organisation Method for Enrichment Evaluations (PROMETHEE) method offers a way to undertake the ranking of a finite set of decision alternatives based on values from a number of criteria. As an outranking method it is a multi-criteria decision aid in the ranking process. This chapter outlines the rudiments of the PROMETHEE method, and applies it in a tutorial problem, including the presentation of example numerical calculations. The inclusion of an uncertainity-based analysis with PROMETHEE in the presented text demonstrates the developmental potential of PROMETHEE. The problem considered throughout the chapter concerns the relative performance ranking of individual police forces in a region of the UK, based on their levels of sanction detections (clean-up rates).