Rank Reversal Research Articles

Supply chain sustainability strategy selection using integrated multi-criteria decision-making method

In recent years, industries have begun taking up proactive leadership and working together with their supply chain partners to implement initiatives for improving the sustainability of the supply chain. Sustainability performance improvements in industries are essential for survival rather than meeting the regulatory and reporting requirements. This paper aims at determining the major sustainability criteria and prioritising the given sustainability strategy sets to achieve the highest sustainability performance. This multi-criteria decision-making (MCDM) problem is solved by the application of the analytic hierarchy process (AHP) and preference ranking organisation method for the enrichment of evaluations (PROMETHEE) techniques in an integrated way. In this study, criteria for comparing the alternatives are identified from literature. The criteria weights are obtained using AHP methodology and are used in the PROMETHEE (I and II) for ranking the strategy sets. The proposed methodology is applied to solve a real-time supply chain decision-making issue of an Indian manufacturing industry. The most sustainable strategy proposal is identified. Sensitivity analysis is carried out on the variations in ranks with respect to the deviation in criteria weights. The minimum deviations in criteria weights that cause rank reversal are also identified from this. It facilitates getting insights into the stability of the ranks to the decision maker.

Comparative Performance of Contradictory and Non-Contradictory Judgement Matrices in AHP Under Qualitative and Quantitative Metrics

Over the years, although AHP has proved its success in various diverse fields, many authors in the literature have also shown its shortcomings, often called as criticisms of AHP. One such criticism is allowing the consideration of contradictory judgement matrices. Such matrices violate the principle of ordinal transitivity and thus there does not exist any ranking of corresponding decision elements which satisfy all the judgements. In this paper, the results of our investigation towards measuring this criticism are further explored and discussed by comparing the quality of priority vector of contradictory judgement matrices and non-contradictory judgement matrices under Rank Reversals and the common frame work of “aggregated deviation”. The results further strengthen the notion of contradictory judgement matrices as a strong criticism of AHP for higher order judgement matrices and necessitate some proper avoidance (if not elimination) procedure for them.

An Iterative Transient Rank Aggregation Technique for Mitigation of Rank Reversal

With the advent of decision science, significant elucidation has been sought in the literature of multi criteria decision making. Often, it is observed that for the same MCDM problem, different methods fetch way-apart ranks and the phenomenon leads to rank reversal. To alleviate this problem, different methodologies like the Borda rule, the Copeland method, the Condorcet method, the statistical Thurstone scaling, and linear programming methods are readily available in the literature. In connection with the same, the authors proposed a novel technique to aggregate the ranks laid by different methods. The algorithm initially assigns equal weights to the methods involved to avoid biasness to a particular method and a simple average rank was obtained. Then, after the separation measures of individual methods with respect to average rank were calculated. Considering the separation measure the higher the weightage, the dynamic weights are ascertained to declare the weighted aggregate rank subjected to the terminal condition which include whether the previous rank equals to the current rank or not. To substantiate the proposed algorithm, a materials selection problem was taken into consideration and solved with the proposed technique. Moreover, the same problem was solved by existing voting techniques like the Borda and the Copeland-Condoract methods. The authors found a correlation of more than 85% between the proposed and existing methodologies.

Supply chain sustainability strategy selection using integrated multi-criteria decision-making method

In recent years, industries have begun taking up proactive leadership and working together with their supply chain partners to implement initiatives for improving the sustainability of the supply chain. Sustainability performance improvements in industries are essential for survival rather than meeting the regulatory and reporting requirements. This paper aims at determining the major sustainability criteria and prioritising the given sustainability strategy sets to achieve the highest sustainability performance. This multi-criteria decision-making (MCDM) problem is solved by the application of the analytic hierarchy process (AHP) and preference ranking organisation method for the enrichment of evaluations (PROMETHEE) techniques in an integrated way. In this study, criteria for comparing the alternatives are identified from literature. The criteria weights are obtained using AHP methodology and are used in the PROMETHEE (I and II) for ranking the strategy sets. The proposed methodology is applied to solve a real-time supply chain decision-making issue of an Indian manufacturing industry. The most sustainable strategy proposal is identified. Sensitivity analysis is carried out on the variations in ranks with respect to the deviation in criteria weights. The minimum deviations in criteria weights that cause rank reversal are also identified from this. It facilitates getting insights into the stability of the ranks to the decision maker.

Using the COMET Method in the Sustainable City Transport Problem: an Empirical Study of the Electric Powered Cars

This paper investigates the problem of sustainable city transport. The significance of this problem is increasing, considering the inevitable increase of air pollution levels in cities. Currently, cars with diesel engines are not admitted to many city centers, and this trend can be expected to be maintained. Moreover, this restriction will be expanded to all combustion cars in the foreseeable future. Therefore, combustion vehicles will eventually be replaced by electric ones.In this paper, the authors use the Characteristic Objects METhod (COMET) to identify a decision-making model for the selection of the best model of car for sustainable city transport. The COMET method is a new approach that is free of the rank reversal phenomenon. The identified model provides information about the significance level of each of the criteria. The presented approach can easily be expanded by using a greater number of criteria and alternatives, depending on the specification of the considered problem. Moreover, the interval numbers concept is proposed as the basic approach to dealing with input data uncertainty by using the COMET method. The presented technique is an efficient and highly accurate approach to make decisions based on experts’ experience.

Rank Reversal Phenomenon According to Normalization Methods in Quantitative Data

Rank Reversal Phenomenon According to Normalization Methods in Quantitative Data

THE PROPERTIES OF HUMAN BEING'S DECISION MAKING

In order to honor Thomas Saaty, the developer of Analytic Hierarchy Process (AHP)/ Analytic Network Process (ANP), and focus on the paper he wrote just before he died, this essay mentions the rank order problem due to the addition/deletion of irrelevant alternatives over which there were controversies in the AHP. Saaty ended the controversies with the explanation that both rank preservation and rank reversal are possible depending on the decision making circumstance. In this paper, we will summarize the three properties that fundamentally act on how to prioritize and aggregate the subjective judgments of human beings in ranking alternatives: comparisons, judgmental dependency and rank order. This essay also mentions the rank order in relation to the cases of addition/deletion of irrelevant criteria from previous studies. This essay closes by expressing gratitude to Thomas Saaty for his work. https://doi.org/10.13033/ijahp.v9i3.528

Optimization methods to estimate alternatives in AHP: The classification with respect to the dependence of irrelevant alternatives

This paper focuses on specific rank reversal phenomena in optimization methods (the least squares method, the chi-square method, etc.) designed to derive preference weights of alternatives from pairwise comparison matrices in the Analytic Hierarchy Process. It is preferable that the most irrelevant alternative had no effect on the ranking of the other alternatives. Unfortunately, it appears that, for many methods, most irrelevant alternatives tend to dictate the rank order of all the remaining alternatives. Respectively, adding some irrelevant alternative may turn the most important alternative into an unimportant one and conversely.We classify the optimization methods with respect to the dependence of irrelevant alternatives and specify all possible “dictatorial” methods, which provide the absolute dictate of very irrelevant alternatives, and all methods, which are free from the dictate of such alternatives. For the dictatorial methods, we propose “weight function” modifications, which prevent the influence of irrelevant alternatives. We show that without the modification, “dictatorial” methods can add confusion and false recommendations in the decision-making process even in the most ordinary decision-making situations.

Investigating rank reversal in reciprocal fuzzy preference relation based on additive consistency: Causes and solutions

Rank reversal is a common phenomenon in decision making. Rank reversal occurs when a new alternative is added to (or removed from) a set of alternatives, which causes change in the ranking order of the alternatives. This paper studies the possible causes of rank reversal in reciprocal preference relation based on additive consistency. Our investigation reveals that inconsistency of information is the main cause of this phenomena in preference relations followed by ranking score aggregation. We propose score aggregation methods to address the phenomenon of rank reversal. The proposed methods are illustrated using numerical examples. The results are better than other tested methods.

Eigenvector Method and Rank Reversal in Group Decision Making Revisited

It has been shown recently that the Eigenvector Method may lead to strong rank reversal in group decision making, that is, the alternative with the highest priority according to all individual vectors may lose its position when evaluations are derived from the aggregated group comparison matrix. We give a minimal counterexample and prove that this negative result is a consequence of the difference of the rankings induced by the right and inverse left eigenvectors.

A Method for Fuzzy Soft Sets in Decision-Making Based on an Ideal Solution

In this paper, a decision model based on a fuzzy soft set and ideal solution approaches is proposed. This new decision-making method uses the divide-and-conquer algorithm, and it is different from the existing algorithm (the choice value based approach and the comparison score based approach). The ideal solution is generated according to each attribute (pros or cons of the attributes, with or without constraints) of the fuzzy soft sets. Finally, the weighted Hamming distance is used to compute all possible alternatives and get the final result. The core of the decision process is the design phase, the existing decision models based on soft sets mostly neglect the analysis of attributes and decision objectives. This algorithm emphasizes the correct expression of the purpose of the decision maker and the analysis of attributes, as well as the explicit decision function. Additionally, this paper shows the fact that the rank reversal phenomenon occurs in the comparison score algorithm, and an example is provided to illustrate the rank reversal phenomenon. Experiments indicate that the decision model proposed in this paper is efficient and will be useful for practical problems. In addition, as a general model, it can be extended to a wider range of fields, such as classifications, optimization problems, etc.

A coupled human–natural system to assess the operational value of weather and climate services for agriculture

Abstract. Recent advances in weather and climate (W&C) services are showing increasing forecast skills over seasonal and longer timescales, potentially providing valuable support in informing decisions in a variety of economic sectors. Quantifying this value, however, might not be straightforward as better forecast quality does not necessarily imply better decisions by the end users, especially when forecasts do not reach their final users, when providers are not trusted, or when forecasts are not appropriately understood. In this study, we contribute an assessment framework to evaluate the operational value of W&C services for informing agricultural practices by complementing traditional forecast quality assessments with a coupled human–natural system behavioural model which reproduces farmers' decisions. This allows a more critical assessment of the forecast value mediated by the end users' perspective, including farmers' risk attitudes and behavioural factors. The application to an agricultural area in northern Italy shows that the quality of state-of-the-art W&C services is still limited in predicting the weather and the crop yield of the incoming agricultural season, with ECMWF annual products simulated by the IFS/HOPE model resulting in the most skillful product in the study area. However, we also show that the accuracy of estimating crop yield and the probability of making optimal decisions are not necessarily linearly correlated, with the overall assessment procedure being strongly impacted by the behavioural attitudes of farmers, which can produce rank reversals in the quantification of the W&C services operational value depending on the different perceptions of risk and uncertainty.

Faecal glucocorticoid metabolite monitoring as a measure of physiological stress in captive and wild vervet monkeys

The development of non-invasive techniques to analyse physiological stress in mammalian species has revolutionised field-based endocrinology. However, careful validation of the methods used to determine faecal glucocorticoid metabolite (fGCM) and other hormone concentrations are required on a species- and sex-specific basis. In this study, we performed an adrenocorticotropic hormone (ACTH) stimulation test on four (two male and two female) captive vervet monkeys (Chlorocebus pygerythrus) to determine the most appropriate enzyme immunoassay (EIA) from a suite of available EIAs. Furthermore, we took advantage of a potentially stressful event in our wild vervet population from Samara Private Game Reserve, South Africa, to examine if an alpha–beta female rank reversal increases the physiological stress of those individuals directly involved, as well as other group members. Both our physiological and biological validation studies revealed that a cortisol assay was the most appropriate EIA for monitoring fGCM alterations in vervet monkeys. In addition, we found that the observed rank-reversal had no significant effect on the physiological stress levels of uninvolved group members. Our study highlights that physiological validation is imperative and, where possible, should be conducted in parallel with a carefully considered biologically-relevant test under natural conditions. Overall, our results provide a necessary step for future studies to examine physiological stress of vervet monkeys via fGCM monitoring by validating a suitable EIA for this species. This paves the way for future research into the health and welfare of both captive and wild vervet monkeys, and will allow researchers to assess the behavioural, social and ecological correlates of physiological stress levels of this species.

Group Decision-Making for Hesitant Fuzzy Sets Based on Characteristic Objects Method

There are many real-life problems that, because of the need to involve a wide domain of knowledge, are beyond a single expert. This is especially true for complex problems. Therefore, it is usually necessary to allocate more than one expert to a decision process. In such situations, we can observe an increasing importance of uncertainty. In this paper, the Multi-Criteria Decision-Making (MCDM) method called the Characteristic Objects Method (COMET) is extended to solve problems for Multi-Criteria Group Decision-Making (MCGDM) in a hesitant fuzzy environment. It is a completely new idea for solving problems of group decision-making under uncertainty. In this approach, we use L-R-type Generalized Fuzzy Numbers (GFNs) to get the degree of hesitancy for an alternative under a certain criterion. Therefore, the classical COMET method was adapted to work with GFNs in group decision-making problems. The proposed extension is presented in detail, along with the necessary background information. Finally, an illustrative numerical example is provided to elaborate the proposed method with respect to the support of a decision process. The presented extension of the COMET method, as opposed to others’ group decision-making methods, is completely free of the rank reversal phenomenon, which is identified as one of the most important MCDM challenges.

A new extension to PROMETHEE under intuitionistic fuzzy environment for solving supplier selection problem with linguistic preferences

This paper presents a new two-tier decision making framework with linguistic preferences for scientific decision making. The major reason for adopting linguistic preference is to ease the process of rating of alternatives by allowing decision makers (DMs) to strongly emphasize their opinion on each alternative. In the first tier, aggregation is done using a newly proposed operator called linguistic based aggregation (LBA), which aggregates linguistic terms directly without making any conversion. The main motivation for this proposal is driven by the previous studies on aggregation theory which reveals that conversion leads to loss of information and formation of virtual sets which are no longer sensible and rational for decision making process. Secondly, in the next tier, a new ranking method called IFSP (intuitionistic fuzzy set based PROMETHEE) is proposed which is an extension to PROMETHEE (preference ranking organization method for enrichment evaluation) under intuitionistic fuzzy set (IFS) context. Unlike previous ranking methods, this ranking method follows a new formulation by considering personal choice of the DMs over each alternative. The main motivation for such formulation is derived from the notion of not just obtaining a suitable alternative but also coherently satisfying the DMs’ viewpoint during decision process. Finally, the practicality of the framework is tested by using supplier selection (SS) problem for an automobile factory. The strength and weakness of the proposed LBA-IFSP framework are verified by comparing with other methods under the realm of theoretical and numerical analysis. The results from the analysis infer that proposed LBA-IFSP framework is rationally coherent to DMs’ viewpoint, moderately consistent with other methods and highly stable and robust against rank reversal issue.

Rank reversal aversion inhibits redistribution across societies

Research has shown that people dislike inequality. However, in a cross-cultural experiment, Zhou and colleagues show that, from a young age, people are unwilling to redistribute resources between individuals if this reverses an existing hierarchy.

Behavioural economics: Preserving rank as a social norm

Experiments show that people dislike inequality, but are they willing to overturn established hierarchies to achieve income equality? A cross-cultural experiment shows that from a young age humans exhibit rank reversal aversion when redistributing resources between the rich and the poor, suggesting that hierarchy preservation is a social norm.

Intraspecific variability and reaction norms of forest understorey plant species traits

Summary Trait‐based models of ecological communities typically assume intraspecific variation in functional traits is not important, although such variation can change species trait rankings along gradients in resources and environmental conditions, and thus influence community structure and function. We examined the degree of intraspecific relative to interspecific variation, and reaction norms of 11 functional traits for 57 forest understorey plant species, including: intrinsic water‐use efficiency (iWUE), Δ15N, five leaf traits, two stem traits and two root traits along gradients in light, nitrogen, moisture and understorey cover. Our results indicate that interspecific trait variation exceeded intraspecific variation by at least 50% for most, but not all traits. Intraspecific variation in Δ15N, iWUE, leaf nitrogen content (LNC) and root traits was high (47–70%) compared with most leaf traits and stem traits (13–38%). Δ15N varied primarily along gradients in abiotic conditions, while light and understorey cover were relatively less important. Intrinsic water‐use efficiency was related primarily to light transmission, reflecting increases in photosynthesis relative to stomatal conductance. Leaf traits varied mainly as a function of light availability, with some reaction norms depending on understorey cover. Plant height increased with understorey cover, while stem‐specific density was related primarily to light. Resources, environmental conditions and understorey cover did not contribute strongly to the observed variation in root traits. Gradients in resources, environmental conditions and competition all appear to control intraspecific variability in most traits to some extent. However, our results suggest that species cross‐over (i.e. trait rank reversals) along the gradients measured here are generally not a concern. Intraspecific variability in understorey plant species traits can be considerable. However, trait data collected under a narrow range of environmental conditions appears sufficient to establish species rankings and scale between community and ecosystem levels using trait‐based models. Investigators may therefore focus on obtaining a sufficient sample size within a single set of conditions rather than characterizing trait variation across entire gradients to optimize sampling efforts. A lay summary is available for this article.

A modified-SAW for network selection in heterogeneous wireless networks

In the context of heterogeneous networks, users with multi-mode terminals can connect to different radio access technologies such as 802.16, 802.11, HSPA and LTE at the same time. The challenge here is to achieve the Always Best Connected ABC concept; the main issue is the automatic choice of the suitable Radio Access Technology RAT from the list of the available RATs. This decision is called the network selection. In this paper, we propose a modified-SAW function to deal with the drawbacks of the existing solutions. Indeed, the existing MADMs methods suffer mainly from the famous problem of rank reversal once an alternative is added or removed, other problems occur in the legacy MADMs such as the sensitiveness to the user preference in TOPSIS; the penalization of the alternatives with poor attributes values in WPM. Our simulations brought out the lakes of the traditional MADMs approach in the context of network selection. We modify the SAW method intelligently and we use it to solve the network selection problem. Finally, we compare the performance of our solution with the previous works in different scenarios; the simulations show that our proposal outperforms the other existing methods.

Avoiding Ranking Contradictions in Human Development Index Using Goal Programming

This paper builds on the extensive literature of the rank reversal issue in multi-criteria decision making (MCDM) techniques. It is a continuation of the study of Sayed et al. (Soc Indic Res 123(1):1–27, 2015) that exhibited this problem in the human development index (HDI) framework. The proposed methodology, the Goal Programming Benefit-of-the-Doubt (GP-BOD), aims to overcome this problem and obtain consistent and stable rankings. For investigating the credibility of the proposed method in solving this issue, it has been applied to the HDI dataset in 2012. The resulted HDI rankings are compared with those evaluated from eleven overlapping sub-groups that are internationally categorized based on geographic regions and income levels. The results show a solution to the ranking contradictions problem. Among other merits, the results prove two additional features of the proposed GP-BOD model. First, the resulted countries’ rankings are distinguishable and absolutely tie-free. This enhances the discriminating power of the proposed rank preservation model. Second, the GP-BOD weights are evaluated on a common base to compare all countries on the same scale. Moreover, a lower bound is endogenously imposed on these weights to avoid the problem of zero weights. Finally, the validity of the proposed GP-BOD technique has been thoroughly examined using sensitivity tests. The results show stability in the rankings when different methods of normalization and weighting are applied.