Ordered Weighted Averaging Research Articles

Enhancing Edge Attack Strategy via an OWA Operator-Based Ensemble Design in Real-World Networks.

Network disintegration has been an important research hotspot in complex networks for a long time. From the perspective of node attack, researchers have devoted to this field and carried out numerous works. In contrast, the research on edge attack strategy is insufficient. This paper comprehensively evaluates the disintegration effect of each structural similarity index when they are applied to the weighted-edge attacks model. Experimental results show that the edge attack strategy based on a single similarity index will appear limited stability and adaptability. Thus, motivated by obtaining a stable disintegration effect, this paper designs an edge attack strategy based on the ordered weighted averaging (OWA) operator. Through final experimental results, we found that the edge attack strategy proposed in this paper not only achieves a more stable disintegration effect on eight real-world networks, but also significantly improves the disintegration effect when applied on a single network in comparison with the original similarity index.

Developing a framework for water right allocation in inter-basin water transfer systems under uncertainty: The Solakan–Rafsanjan water transfer experience

Abstract In this article, a methodology is presented for water allocation to demands in new inter-basin water transfer projects. In this methodology, by proposing some criteria such as universality, equity, enforceability, adaptivity, and exclusivity, a new scheme is developed in order to determine the share of water users. For taking the available water uncertainty into account, the concept of conditional value at risk (CVaR) is used, in which the goal is to minimize an indicator of loss in low flow conditions. Also, in order to estimate the relative weights of criteria, a novel method is introduced. This method is founded on two essential information aggregation operators, namely, hybrid weighted averaging (HWA) and ordered weighted averaging (OWA). The significant property of this method is that the weighting process is done by considering the relative weights of experts and the viewpoint of the analyzer jointly. The results of the CVaR-based model are compared with those obtained from a long-term water allocation optimization model with monthly time steps. The performance of the proposed methodology is evaluated using available data from the Solakan–Rafsanjan water transfer project.

Multiple criteria group decision analysis using a Pythagorean fuzzy programming model for multidimensional analysis of preference based on novel distance measures

The preference concerning criteria or alternatives from decision makers (DMs) is a critical element in multicriteria group decision making (MCGDM). The linear programming technique for the multidimensional analysis of preference (LINMAP) is the most influential technique in adjusting the error between the objective assessments on criteria and subjective preferences on alternatives via optimal programming for MCGDM problems. Considering the higher capacity for expressing the uncertainties of human inherent preferences of the Pythagorean fuzzy (PF) sets, this article explores the LINMAP technique for MCGDM problems under the PF scenario. First, we present a novel (squared) Euclidean distance measure for the PF sets since it possesses captivating characteristics of the PF sets and the distance measure. Second, we define the preference degrees over alternatives that are expressed as PF sets and calculate their magnitudes via relative distances, which are used to calculate the consistency and inconsistency indices. Third, the ordered weighted averaging (OWA) operator under the normal distribution is introduced to obtain the weights of DMs to reduce the influence of unfair arguments. Fourth, we construct the biobjective PF-LINMAP model for minimizing both the error extents and the overall distances to the PF positive ideal solution for the minimum error and global optimum. Then, we obtain the optimal criterion weights and ranks of the candidates via relative closeness indices. Finally, we perform our PF-LINMAP method to choose the optimal green supplier, and we implement the sensitivity analysis and comparative analysis with those of the available PF-LINMAP methods to verify the feasibility and superiority of our approach.

Integrated data envelopment analysis and multicriteria decision-making ranking approach based on peer-evaluations and subjective preferences: case study in banking sector

PurposeThis paper is focused on developing an integrated algorithmic approach named as data envelopment analysis and multicriteria decision-making (DEA-MCDM) for ranking decision-making units (DMUs) based on cross-efficiency technique and subjective preference(s) of the decision maker.Design/methodology/approachSelf-evaluation in data envelopment analysis (DEA) lacks in discrimination power among DMUs. To fix this, a cross-efficiency technique has been introduced that ranks DMUs based on peer-evaluation. Different cross-efficiency formulations such as aggressive and benevolent and neutral are available in the literature. The existing ranking approaches fail to incorporate subjective preference of “one” or “some” or “all” or “most” of the cross-efficiency evaluation formulations. Therefore, the integrated framework in this paper, based on DEA and multicriteria decision-making (MCDM), aims to present a ranking approach to incorporate different cross-efficiency formulations as well as subjective preference(s) of decision maker.FindingsThe proposed approach has an advantage that each of the aggressive, benevolent and neutral cross-efficiency formulations contribute to select the best alternative among the DMUs in a MCDM problem. Ordered weighted averaging (OWA) aggregation is applied to aggregate final cross-efficiencies and to achieve complete ranking of the DMUs. This new approach is further illustrated and compared with existing MCDM approaches like simple additive weighting (SAW) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to prove its validity in real situations.Research limitations/implicationsThe choice of cross-efficiency formulation(s) as per subjective preference of the decision maker and different orness levels lead to different aggregated scores and thus ranking of the DMUs accordingly. The proposed ranking approach is highly useful in real applications like R and D projects, flexible manufacturing systems, electricity distribution sector, banking industry, labor assignment and the economic environmental performances for ranking and benchmarking.Practical implicationsTo prove the practical applicability and robustness of the proposed integrated DEA-MCDM approach, it is applied to top twelve Indian banks in terms of three inputs and two outputs for the period 2018–2019. The findings of the study (1) ensure the impact of non-performing assets (NPAs) on the ranking of the selected banks and (2) are enormously valuable for the bank experts and policy makers to consider the impact of peer-evaluation and subjective preference(s) in formulating appropriate policies to improve performance and ranks of underperformed banks in competitive scenario.Originality/valueTo the best of the authors’ knowledge, this is the first study that has integrated both DEA and MCDM via OWA aggregation to present a ranking approach that can incorporate different cross-efficiency formulations and subjective preference(s) of the decision maker for ranking DMUs.

A Novel Fuzzy Time Series Forecasting Model Based on the Hybrid Wolf Pack Algorithm and Ordered Weighted Averaging Aggregation Operator

The fuzzy time series has received extensive attention since it was proposed and it has been widely used in various practical applications. This study proposes a new fuzzy time series forecasting model which considers a hybrid wolf pack algorithm (HWPA) and an ordered weighted averaging (OWA) aggregation operator for fuzzy time series. The HWPA is adopted to obtain a suitable partition of the universe of discourse to promote the forecasting performance. Furthermore, the improved OWA aggregation method is applied to make the aggregation of historical information more practical. To overcome the deficiency of slow convergence speed and easy to entrap into the local extremum of the wolf pack algorithm (WPA), the chemotactic behavior and elimination–dispersal behavior of bacterial foraging optimization (BFO) are employed to optimize the scouting behavior of WPA. The actual enrollments data of the University of Alabama and Taiwan Futures Exchange (TAIFEX) are utilized as the benchmark data and the computational results of both training and testing phases all indicate that the new forecasting model outperforms other existing models. The robustness of the proposed model is also tested and the robust results can be obtained when the historical data are inaccurate.

A Dynamic multi-sensor data fusion approach based on evidence theory and WOWA operator

Multi-sensor data fusion (MSDF) problems have attracted widespread attention recently. However, it is still an open issue about how to make the fusion process effectively even if the collected data conflict due to several unpredictable reasons. Moreover, most existing approaches mainly concentrated on the distinction of evidence sources, which cannot well consider the feature of individual belief degree and the associated preference of decision-makers. To address such an issue, a dynamic MSDF method based on evidence theory and weighted ordered weighted averaging (WOWA) operator is proposed in this study. A numerical example is analyzed to demonstrate its whole calculation procedure. Two simulation experiments, composed of a motor rotor fault diagnosis and an insulator string target recognition application, are also mentioned to illustrate its effectiveness and applied value. The results show that the proposed methodology can enhance the fusion accuracy in the constrained scenarios with the consideration of preference relation.

Equitable Optimization for Multicast Communication

Multicast communication is characterized by the multiplicity of streams defining different groups, where each stream has multiple sources. A multicast communication tends to flood the network with a large number of flows that can overload some nodes and unload others. This imbalance in the load distribution weakens network performance and could produce bottlenecks around overloaded nodes. We propose in this article an approach based on a combination of a flow approach and a multi-agent optimization to resolve the load balancing issue of multicast communication. We use ordered weighted average (OWA), a multi-criteria optimization method, to balance the degree of the nodes, ensuring a balanced load distribution across the network. The experiments conducted on a series of networks show that our approach provides a better equitable load assignment.

Modeling an Uncertain Productivity Learning Process Using an Interval Fuzzy Methodology

Existing methods for forecasting the productivity of a factory are subject to a major drawback—the lower and upper bounds of productivity are usually determined by a few extreme cases, which unacceptably widens the productivity range. To address this drawback, an interval fuzzy number (IFN)-based mixed binary quadratic programming (MBQP)–ordered weighted average (OWA) approach is proposed in this study for modeling an uncertain productivity learning process. In the proposed methodology, the productivity range is divided into the inner and outer sections, which correspond to the lower and upper membership functions of an IFN-based fuzzy productivity forecast, respectively. In this manner, all actual values are included in the outer section, whereas most of the values are included within the inner section to fulfill different managerial purposes. According to the percentages of outlier cases, a suitable forecasting strategy can be selected. To derive the values of parameters in the IFN-based fuzzy productivity learning model, an MBQP model is proposed and optimized. Subsequently, according to the selected forecasting strategy, the OWA method is applied to defuzzify a fuzzy productivity forecast. The proposed methodology has been applied to the real case of a dynamic random access memory factory to evaluate its effectiveness. The experimental results indicate that the proposed methodology was superior to several existing methods, especially in terms of mean absolute error, mean absolute percentage error, and root mean square error in evaluating the forecasting accuracy. The forecasting precision achieved using the proposed methodology was also satisfactory.

Detection of desirable areas for urban growth through GIS and OWA: the case of Culiacan and Navolato

Due to an accelerated growth of artificial surfaces, an increasing interest in the changes on land use has been detected in the last decade, making it necessary to propose models able to create plans of optimal and sustainable development. The goal of this work is to identify the potentially most suitable spaces for urban growth, through geospatial simulations which integrate the Geographic Information Systems and OWA (Ordered Weighted Average). We propose a sensitivity analysis methodology to the results in order to assess their robustness with respect to the input values’ weights variability, based on histograms and distances classification. Finally, the selection proposal shows a considerable percentage according to the planned area.

Participatory modelling for poverty alleviation using fuzzy cognitive maps and OWA learning aggregation.

Participatory modelling is an emerging approach in the decision-making process through which stakeholders contribute to the representation of the perceived causal linkages of a complex system. The use of fuzzy cognitive maps (FCMs) for participatory modelling helps policy-makers develop dynamic quantitative models for strategising development interventions. The aggregation of knowledge from multiple stakeholders provides consolidated and more reliable results. Average aggregation is the most common aggregation method used in FCMs-based modelling for weighted interconnections between concepts. This paper proposes a new aggregation method using learning OWA (ordered weighted averaging) operators for aggregating FCM weights assigned by various stakeholders. Besides, we report a comparative analysis of 'OWA learning aggregation' with the conventional average aggregation method, while evaluating the theory of change for the world's most extensive poverty alleviation programme in India. The results of the FCMWizard web-based tool show that the proposed method provides an opportunity to policy-makers for evaluating outcomes of proposed policies while addressing social resilience and economic mobility.

Extended and infinite ordered weighted averaging and sum operators with numerical examples

This study discusses some variants of Ordered WeightedAveraging (OWA) operators and related information aggregation methods. Indetail, we define the Extended Ordered Weighted Sum (EOWS) operator and theExtended Ordered Weighted Averaging (EOWA) operator, which are applied inscientometrics evaluation where the preference is over finitely manyrepresentative works. As contrast, we also define the Infinite OrderedWeighted Sum (InOWS) operator and the Infinite Ordered Weighted Averaging(InOWA) operator, which are more suitable for the correspondingscientometrics evaluation where all of works of scholars are considered. Wealso define the family of Infinite Gaussian maxitive OWA weights functionand the family of Infinite Gaussian OWA weights function, and discuss someof their mathematical properties. Some illustrative examples, comparisonsand figures are provided to better expound their applicability inscientometrics evaluation.

Priority areas for forest restoration aiming at the maintenance of water resources in a basin in the Cerrado/Amazon ecotone, Brazil

Mato Grosso is one of the states with the greatest agricultural importance in Brazil and represents one of the main agricultural frontiers in the world. The expansion of these frontiers, along with deforestation, livestock farming and urbanization, leads to a new dynamics with significant environmental impacts on the availability of water resources. In this context, the objective of the study was to evaluate methods of multi-criteria analysis and map priority areas for forest restoration in the Upper Teles Pires basin – Mato Grosso, Brazil, in order to support decision makers and the maintenance of water resources, according to the quantity and quality of water. Multi-criteria Analysis (MCA), with integration of the criteria of proximity to forest, slope, erodibility and proximity to surface water, was used. The criteria maps were aggregated using the Ordered Weighted Averaging (OWA), with varying risk and tradeoff, and Weighted Linear Combination (WLC) methods. From the MCA it was possible to successfully identify potential areas for forest restoration for the basin under study. The WLC, OWA2 and OWA6 methods showed a good solution for decision-making as they respectively set 16.38%, 22.73% and 12.79% of the total area of the basin as of very high priority.

Approximating combinatorial optimization problems with the ordered weighted averaging criterion

This paper deals with combinatorial optimization problems with K cost scenarios, inducing K linear objectives. The popular Ordered Weighted Averaging (OWA) criterion is used to aggregate the objectives and compute a solution. It is well-known that minimizing OWA for most basic combinatorial problems is weakly NP-hard even if the number of scenarios K equals two, and strongly NP-hard when K is a part of the input. In this paper, the problem with nonincreasing weights in the OWA criterion and a large K is first considered. A method of reducing the number of scenarios, by appropriately aggregating the costs before solving the problem, is proposed. It is shown that an optimal solution to the reduced problem has a guaranteed worst-case approximation ratio. Some new approximation results for the Hurwicz criterion, which is a special case of OWA, are also presented.

Reliability Assessment of Software System Using IFS and OWA-Tree Analysis

To address the software design and development, reliability assessment is considered as crucial and most important task. Several studies have been directed towards reliability assessment approaches for obtaining highly reliable software product. In conventional reliability theory, failure probability of any component is assumed as an exact value but in actuality it’s not possible to get failure probability precisely. In this study, we have proposed an approach to assess the reliability of a software system with vague failure rate of the components as the given information might be incomplete or uncertain. It is a bottom–top methodology which includes the combination of intuitionistic fuzzy set (IFS) theory and ordered weighted averaging (OWA) tree analysis. Using IFS, we are able to come over the vagueness in the failure rate data and by using OWA-tree, we incorporate the subjectivity in the opinion of software developers with respect to selection of module. Further, for the illustration of the proposed approach one numerical example has been discussed and software reliability is assessed based upon different orness level.

Parameterized Algorithms for Finding a Collective Set of Items

We extend the work of Skowron et al. (AIJ, 2016) by considering the parameterized complexity of the following problem. We are given a set of items and a set of agents, where each agent assigns an integer utility value to each item. The goal is to find a set of k items that these agents would collectively use. For each such collective set of items, each agent provides a score that can be described using an OWA (ordered weighted average) operator and we seek a set with the highest total score. We focus on the parameterization by the number of agents and we find numerous fixed-parameter tractability results (however, we also find some W[1]-hardness results). It turns out that most of our algorithms even apply to the setting where each agent has an integer weight.

Covariances with OWA operators and Bonferroni means

The covariance is a statistical technique that is widely used to measure the dispersion between two sets of elements. This work develops new covariance measures by using the ordered weighted average (OWA) operator and Bonferroni means. Thus, this work presents the Bonferroni covariance OWA operator. The main advantage of this approach is that the decision maker can underestimate or overestimate the covariance according to his or her attitudes. The article further generalizes this formulation by using generalized and quasi-arithmetic means to obtain a wide range of particular types of covariances, including the quadratic Bonferroni covariance and the cubic Bonferroni covariance. The paper also considers some other extensions by using induced aggregation operators in order to use complex reordering processes in the analysis. The work ends by studying the applicability of these new techniques to real-world problems and presents an illustrative example of a research and development (R&D) investment problem.

Legal document similarity: a multi-criteria decision-making perspective.

The vast volume of documents available in legal databases demands effective information retrieval approaches which take into consideration the intricacies of the legal domain. Relevant document retrieval is the backbone of the legal domain. The concept of relevance in the legal domain is very complex and multi-faceted. In this work, we propose a novel approach of concept based similarity estimation among court judgments. We use a graph-based method, to identify prominent concepts present in a judgment and extract sentences representative of these concepts. The sentences and concepts so mined are used to express/visualize likeness among concepts between a pair of documents from different perspectives. We also propose to aggregate the different levels of matching so obtained into one measure quantifying the level of similarity between a judgment pair. We employ the ordered weighted average (OWA) family of aggregation operators for obtaining the similarity value. The experimental results suggest that the proposed approach of concept based similarity is effective in the extraction of relevant legal documents and performs better than other competing techniques. Additionally, the proposed two-level abstraction of similarity enables informative visualization for deeper insights into case relevance.

On WA Expressions of Induced OWA Operators and Inducing Function Based Orness With Application in Evaluation

Induced ordered weighted averaging (OWA) operators are important extensions of OWA operators and can suit more different decision environments. However, when practitioners perform induced OWA operators, they frequently do not consider the tied values in inducing information, therein lay the problem of possible non-uniqueness of aggregation results. From a novel perspective that can easily remove this flaw, this study uses a systematical frame to better redefine induced OWA operators, which not only covers all the spirits of the original definition of induced OWA operator but also extends it to some wider understandings and usages. One significant feature related to this frame is that we can express induced OWA operators fully in terms of weighted averaging operators, making the usages and expressions of induced OWA operators much more flexible and stricter. In detail, we propose several new definitions, such as positioning transformation, permutation-based orness, and inducing function based orness. As an example of flexibly formulating complex preferences under the proposed concepts, a novel investigators-consultants-decision maker (ICD) two-stage IOWA evaluation model is clearly proposed and illustrated.

Multi-Resource Allocation for Network Slicing

Among the novelties introduced by 5G networks, the formalization of the `network slice' as a resource allocation unit is an important one. In legacy networks, resources such as link bandwidth, spectrum, computing capacity are allocated independently of each other. In 5G environments, a network slice is meant to directly serve end-to-end services, or verticals: behind a network slice demand, a tenant expresses the need to access a precise service type, under a fully qualified set of computing and network requirements. The resource allocation decision encompasses, therefore, a combination of different resources. In this paper, we address the problem of fairly sharing multiple resources between slices, in the critical situation in which the network does not have enough resources to fully satisfy slice demands. We model the problem as a multi-resource allocation problem, proposing a versatile optimization framework based on the Ordered Weighted Average (OWA) operator, that takes into account different fairness approaches. We show how, adapting the OWA utility function, our framework can generalize classical single-resource allocation methods, existing multi-resource allocation solutions at the state of the art, and implement novel multi-resource allocation solutions. We compare analytically and by extensive simulations the different methods in terms of fairness and system efficiency.

An Integrated Feature-Based Failure Prognosis Method for Wind Turbine Bearings

In North America, many utility-scale turbines are approaching the half-way point of their anticipated initial lifespan. Accurate remaining useful life (RUL) estimation can provide wind farm owners insight into how to optimize the life and value of their farm assets. An improved understanding of the RUL of turbine components is particularly essential as many owners consider retiring, life-extending, or repowering their farms. In this article, an integrated prognosis method based on signal processing and an adaptive Bayesian algorithm is proposed to predict the RUL of various faulty bearings in wind turbines. The signal processing leverages feature extraction, feature selection, and signal denoising to detect the dynamics of various failures. Then, RUL of the faulty bearings is forecast via the adaptive Bayesian algorithm using the extracted features. Finally, a new fusion method based on an ordered weighted averaging (OWA) operator is applied to the RUL obtained from the features to improve accuracy. The efficacy of the method is evaluated using data from historical failures across three different Canadian wind farms. Experimental test results indicate that the OWA operator delivers a higher accuracy for RUL prediction compared to the other feature-based methods and Choquet integral fusion technique.