Uncertain Preferences Research Articles

Sustainable battery supplier evaluation of new energy vehicles using a distributed linguistic outranking method considering bounded rational behavior

Due to policy promotion towards new energy vehicles, battery supplier evaluation, as a critical multi-faceted decision problem, is the foundation of the good quality of products or service and further long-term development of enterprises. Most of methods have been proposed to measure the performance of potential candidates from the sustainable dimension, but they may ignore resilient and smart criteria, i.e., risk resistance capacity and smart technologies application, relating to normal supply and product reliability. Additionally, prior studies mainly report on the priority of suppliers from perfect rationality of the decision maker, but they pay little attention to indifference and incomparability relations between suppliers and bounded rationality of the decision maker. Therefore, this paper proposes an outranking based multi-criteria behavioral decision making method, named as prospect theory-based distributed linguistic ORESTE method to portray three outranking relations, preference, indifference and incomparability, from the view of bounded rationality of the decision maker. This approach adopts linguistic distribution to reveal the uncertain preference along with good flexibility and adaptability. It is applied to the battery supplier selection in new energy vehicles enterprises. Comparative analysis indicates the availability and superiority and sensitivity analysis verifies its stability.

Consensus measure for interval multiplicative preference relations from a probability distribution perspective

Interval multiplicative preference relations are an extension of multiplicative preference relations, which are commonly used by experts to express their uncertain preference information about objects in group decision making. This paper proposes a new consensus measure of interval multiplicative preference relations in the group decision making from a probability distribution perspective. First, the collective interval multiplicative preference relation is obtained by assuming that the interval obeys the lognormal distribution. Then, a new consensus measure of interval multiplicative preference relations is proposed. Finally, a numerical example is given to illustrate the proposed approach.

Normal Wiggly Probabilistic Hesitant Fuzzy Information for Environmental Quality Evaluation

As a valuable tool for representing uncertain information, probabilistic hesitant fuzzy sets (PHFS) have gained considerable recognition and in-depth discussion in recent years to increase the flexibility and manifest hesitant information in decision-making problems. However, decision-makers (DMs) cannot express all preferences only through a few probabilistic terms in actual decision-making. Much critical information is hidden behind the original information provided by the DMs. Keeping that in mind, we are interested in mining deeper uncertain information from the original probabilistic hesitant fuzzy evaluation data. To achieve the target, we put forward a novel representation tool called the normal wiggly probabilistic hesitant fuzzy set (NWPHFS) to extract deeper uncertain preferences from original probabilistic information. NWPHFS retains the original evaluation information and carries and assesses the potential uncertain details for increasing the rationality of decision-making outcomes. Herein, we propose some fundamental concepts of NWPHFS, for instance, some elementary operational laws, distance measures between two NWPHFSs, and score function. We also suggest two new aggregation operators, that is, the normal wiggly probabilistic hesitant fuzzy weighted averaging (NWPHFWA) and normal wiggly probabilistic hesitant fuzzy weighted geometric (NWPHFWG). A novel mechanism is proposed here to work out multiattribute decision-making (MADM) in solving normal wiggly probabilistic decision-making problems. Through a practical example of environmental quality assessment, the specific calculation steps of this method are epitomized. Finally, we have demonstrated the feasibility and advancement of the proposed approach via a comprehensive comparative study.

Compromise solutions for stochastic multicriteria acceptability analysis with uncertain preferences and nonmonotonic criteria

AbstractAs one of the most popular multiple criteria decision‐making (MCDM) methods, the stochastic multicriteria acceptability analysis (SMAA) is powerful in dealing with the uncertain preference information of decision makers. However, how to find compromise solutions for MCDM problems remains an open question in the SMAA family. A compromise solution refers to an alternative that is the closest to the ideal solution. In this paper, we propose an improved SMAA method, called the SMAA with compromise solutions (SMAA‐CO), to determine compromise solutions for MCDM problems with the uncertain preferences of decision makers and conflicting criteria. The SMAA‐CO uses nonmonotonic additive value functions to depict decision makers’ ideal values for reference points. We apply the proposed method to capture multiple decision makers’ uncertain preference information. An illustrative example concerning supplier selection in the pulp and paper industry is used to show the applicability of the proposed method. Some managerial insights are given.

On consensus reaching process based on social network analysis in uncertain linguistic group decision making: Exploring limited trust propagation and preference modification attitudes

This paper explores a limited trust propagation-based consensus model considering individual attitude for preference modification in a social networked setting with uncertain preference information. To examine the construction of complete linkages, and the status of decision makers in group decision making, it is assumed that the group size and network density will affect the scale of mediators in the propagation process, then a definition of limited trust propagation is proposed and the propagation efficiency can be introduced. On this basis, we obtain missing trust relationships and individual centrality in network. In the process of consensus reaching, both the decision maker’s original preference and recommendation advice are considered for flexibly modeling the preference modification process: the individual attitude toward modification is determined by a newly introduced measure of comprehensive relative out-degree centrality, showing the degree of willingness to adjust assessments. When the willingness is too low to reach the preset consensus level, a multi-objective programming model is designed to improve the consensus as much as possible. Moreover, the proposed feedback mechanism narrows the individual acceptable modification range based on the previous adjustment rule, so as to simulate the personalized and targeted decision behavior. To guarantee obtaining a collective aggregated preference in a logical and precise manner, a two-stage optimization model composing of comprehensive relative in-degree centrality-based information aggregation and best consistency-based uncertainty elimination, is proposed. A numerical example and comparative analyses are performed to show the validity and feasibility of the proposed model.

Probabilistic Linguistic-Based Group DEMATEL Method with Both Positive and Negative Influences

Decision-making trial and evaluation laboratory (DEMATEL) is a widely accepted factor analysis algorithm for complex systems. The rationality of the evaluation scale is the basis of sound DEMATEL decision-making. Unfortunately, the existing evaluation scales of DEMATEL failed to reasonably distinguish and describe the positive and negative influences between factors. Generally, the positive and negative influences between factors should be considered at the same time. In other words, negative influence between factors should not be directly ignored, which is improper and unrealistic. To better address this issue, we extend the evaluation scale of DEMATEL. We also integrate the scale-based group DEMATEL method with probabilistic linguistic term sets (PLTSs) to increase its effectiveness, which allows experts to express incomplete and uncertain linguistic preferences in DEMATEL decision-making. An experts’ subjective weight adjustment method based on the similarity degree between PLTSs is introduced to determine experts’ weights. Finally, an algorithm of probabilistic linguistic-based group DEMATEL method with both positive and negative influences is summarized, and an example is used to illustrate the proposed method and demonstrate its superiority. Our results demonstrate that the method proposed in this paper deals reasonably with realistic problems.

Research on Projection Gray Target Model Based on FANP-QFD for Weapon System of Systems Capability Evaluation

The construction and evaluation of weapon system of systems (SoS) construction should start from the perspective of demand so that the system construction can better meet the actual demand. Based on the in-depth analysis of SoS architecture, an integrated framework for SoS requirements decomposition and capability evaluation is proposed. First of all, quality function development (QFD) is used to decompose the SoS requirements step-by-step. According to the house of quality mapping from missions to capabilities and performances, SoS evaluation indicator elements are determined and the requirement analysis framework is primarily formed. Second, the fuzzy analytical network process (FANP) is used to capture both symmetrical and asymmetrical relationships between the factors of each level in which priorities are calculated under subjective, vague, and uncertain preference information. And then, weighted by priorities of performance requirements that calculated by integrating FANP and QFD model, a projection gray target model is constructed for SoS capability evaluation under uncertain information and poor data. Finally, the proposed model is verified effectively and accurately by a case study, which provides an integrated method for both SoS requirement analysis and capability evaluation, and lays a theoretical foundation for system design and system optimization. Capability evaluation, fuzzy analytical network process (FANP), gray target model, quality function development (QFD), requirements analysis, weapon system of systems (SoS).

Multi-Objective Faculty Course Assignment Problem with Result and Feedback Based Uncertain Preferences

In this paper, a multi-objective faculty course allocation problem with result analysis and feedback analysis based on uncertain preferences mathematical model is presented. To deal with an uncertain model, three different ranking criteria are being used to develop: a) Expected value, b) Optimistic value, c) Dependent optimistic value criterion. These mathematical models are transformed into their corresponding deterministic forms using the basic concepts of uncertainty theory. The deterministic model of DOCM consists of fractional objectives which are converted into their linear form using Charnes and Cooper’s transformation. These deterministic formulations MOFCAP are converted into a single objective problem by using the fuzzy programming technique with linear and exponential membership functions. Further, the single objective problem for all the defined models is solved in the Lingo 18.0 software to derive the Pareto-optimal solution. The sensitivity of the models is also performed to examine the variation in the objective function due to the variation in parameters. Finally, a numerical example is given to exhibit the application and algorithm of the models.

Efficient Computation of Top-K Skyline Objects in Data Set With Uncertain Preferences

Skyline recommendation with uncertain preferences has drawn AI researchers' attention in recent years due to its wide range of applications. The naive approach of skyline recommendation computes the skyline probability of all objects and ranks them accordingly. However, in many applications, the interest is in determining top-k objects rather than their ranking. The most efficient algorithm to determine an object's skyline probability employs the concepts of zero-contributing set and prefix-based k-level absorption. The authors show that the performance of these methods highly depends on the arrangement of objects in the database. In this paper, the authors propose a method for determining top-k skyline objects without computing the skyline probability of all the objects. They also propose and analyze different methods of ordering the objects in the database. Finally, they empirically show the efficacy of the proposed approaches on several synthetic and real-world data sets.

Priority weights acquisition of linear uncertain preference relations and its application in the ranking of online shopping platforms

The linear uncertain preference relations (LUPRs) uses an uncertain variable to represent the decision-maker’s pairwise comparison judgment of the scheme. The variable is subject to the linear uncertain distribution, which is the extension of the traditional fuzzy preference relations (FPRs) and interval fuzzy preference relations (IFPRs). This paper proposes a group decision modeling problem, constructs the priority weights acquisition models of the additively and multiplicatively consistent LUPRs, and especially solves the problem that the weight solution is negative value or no solution by using traditional methods to acquire weights in consistent FPRs and IFPRs. Based on these two types of consistent structure of LUPRs, this study constructs the crisp number, interval number weight solving models of LUPRs and the group decision ranking models with LUPRs. The results show that these new models are suitable for solving the weight vector of traditional FPRs and IFPRs. The case of online shopping platform selection compares the results obtained by various methods of calculating weights, further illustrating the effectiveness and rationality of the new methods.

The Application of Probabilistic Hesitation Fuzzy Lingustic in Urban Livability

With the continuous improvement of residents' living conditions and the improvement of spiritual civilization construction, urban livability has become the focus of attention of the government and urban residents, as well as a hot topic of research by experts and scholars at home and abroad. How to choose a suitable city to live in has become a group decision-making (GDM) problem that we have to deal with. However, the changeable decision environment leads to the fact that the decision information of the above GDM problem cannot be expressed by traditional display values. Therefore, we choose the probabilistic hesitation fuzzy linguistic term set (PHFLTS) and then study the consensus among preference relations (PRs). This paper will study the group consensus model based on PHFLTS. At present, it has been studied in supply chain management, public opinion prediction and other fields. This paper first defines the concept of PHFLTS and distance measure, then defines the similarity measure and the consistency index (CI) based on the distance measure, constructs the group consensus level (GCI), can express the uncertain preference information given by the decision maker more comprehensively. Finally, specific example is given to rank the cities and the future research direction is prospected.

Software reliability allocation incorporating Pythagorean fuzzy theory and AHP

Systematic management in the early stage of development helps the software firms to develop a qualitative software product. One of the crucial tasks in the software design phase of software development life cycle is to assign reliabilities to modules based on the overall reliability goal. AHP has been extensively used to allocate reliability among modules on the basis of preferences given by the decision-maker, who can be an industry expert or senior level IT personnel. Their opinions are obtained in linguistic terms and later converted to crisp values for comparison at various levels. This approach incorporates vague decisions, incomplete information and uncertain preferences during decision-making. In this paper, we discuss an allocation method where preferences are assigned in terms of Pythagorean fuzzy numbers based on a hierarchy that relates the users, software engineers and programmer expectations for the software system. The methodology and concepts used have been discussed in detail. The proposed approach has been implemented through an example problem.

Belief-Based Best Worst Method

The Best-Worst Method (BWM) is a Multi-Criteria Decision Making (MCDM) method that has recently been introduced. The original BWM assumes that decision-makers are always certain about their judgments even if, in reality, decision-makers often express uncertain preferences. To deal with uncertainty, we introduce a belief structure in the BWM, a concept involving the preference degree adopted via Dempster-Shafer theory. A new approach is proposed to allow BWM to cope with this kind of information, where the level of belief in preferences being expressed is taken into account. In addition, an inconsistency measurement and an uncertainty measurement are proposed for the belief-based BWM, providing the foundation for a reliability degree of the decision-makers, after which the belief-based BWM is extended to include a group of decision-makers. Based on their reliability degrees and the weights of the criteria obtained from the various individuals, the overall criteria weights can be aggregated accordingly. Finally, a case study on the assessment of the infrastructure project criteria system in Indonesia is provided to demonstrate the applicability and feasibility of the proposed method.

Linear uncertain extensions of the minimum cost consensus model based on uncertain distance and consensus utility

Uncertainty theory adopts the belief degree and uncertainty distribution to ensure good alignment with a decision-maker’s uncertain preferences, making the final decisions obtained from the consensus-reaching process closer to the actual decision-making scenarios. Under the constraints of the uncertain distance measure and consensus utility, this article explores the minimum-cost consensus model under various linear uncertainty distribution-based preferences. First, the uncertain distance is used to measure the deviation between individual opinions and the consensus through uncertainty distributions. A nonlinear analytical formula is derived to avoid the computational complexity of integral and piecewise function operations, thus reducing the calculation cost of the uncertain distance measure. The consensus utility function defined in this article characterizes the adjustment value and degree of aggregation of individual opinions. Three new consensus models are constructed based on the consensus utility and linear uncertainty distribution. The results show that, in complex group decision-making contexts, the uncertain consensus models are more flexible than traditional minimum-cost consensus models: compared with the high volatility of the adjusted opinions in traditional deterministic consensus models with crisp number-based preferences, the variation trends of both individual adjusted opinions and the collective opinion with a linear uncertainty distribution are much smoother and the fitting range is closer to reality. The introduction of the consensus utility not only reflects the relative changes of individual opinions, but also accounts for individual psychological changes during the opinion-adjustment process. Most importantly, it reduces the cost per unit of consensus utility, facilitates the determination of the optimal threshold for the consensus utility, and improves the efficiency of resource allocation.

Rating TAs in fuzzy QFD by objective penalty function and fuzzy TOPSIS based on weighted Hamming distance

Quality function deployment (QFD) is an useful tool to solve Multi-criteria decision making, which can translate customer requirements (CRs) into the technical attributes (TAs) of a product and helps maintain a correct focus on true requirements and minimizes misinterpreting customer needs. In applying quality function deployment, rating technical attributes from input variables is a crucial step in fuzzy environments. In this paper, a new approach is developed, which rates technical attributes by objective penalty function and fuzzy technique for order preference by similarity to an ideal solution (TOPSIS) based on weighted Hamming distance under the case of uncertain preference characteristics of decision makers in fuzzy quality function deployment. A pair of nonlinear programming models with constraints and a relevant pair of nonlinear programming models with unconstraints called objective penalty function models are proposed to gain the fuzzy important numbers of technical attributes. Then, this paper compares the fuzzy numbers by fuzzy technique for order preference by similarity to an ideal solution (TOPSIS) method based on weighted Hamming distance in consideration of the uncertain preference characteristics of decision makers. To end with, the developed method is examined with the numerical examples.

Discrete-valued belief structures combination and normalization using evidential reasoning rule

Discrete-valued belief structures (DBSs) (known as discrete belief structures) are universal in real life, differ from precise-valued belief structures, and interval-valued belief structures (IBSs). However, the combination of different discrete belief structures presents a problem that has yet to be solved. Therefore, this study investigated the respective counter-intuitive types of behavior associated with the combination of discrete belief structures within the frameworks of the Dempster-Shafer theory. (DST) evidential reasoning (ER) for the purpose of constructing a more general method for the combination and normalization of discrete evidence. Finally, an experimental application is provided to indicate that the proposed method is suitable for combining and normalizing conflict-free/conflicting discrete evidence, and can effectively solve problems involving group decision-making (GDM) with uncertain preference ordinals, such as in a software selection problem.

Decision-Making Approach of Two-Sided Matching of Supply and Demand of Logistics Service Based on the Uncertain Preference Ordinal

Aiming at the problem of matching logistics service supply and demand, this paper proposes a two-sided matching decision model of logistics service supply and demand based on the uncertain preference ordinal. In this model, the uncertain preference ordinal information is first expressed by the interval numbers of the logistics service supply and demand, and it is converted into the satisfaction degree of supply and demand matching uncertainty expressed by the interval number. Then, a multiobjective optimal matching model is constructed based on the largest overall satisfaction of the logistics service supply and demand side and the smallest satisfaction variance of the supply- and demand-side individual, and the multiobjective solution algorithm is designed based on nondominated sorting genetic algorithm-III (NSGA-III). Interval numbers are used to sort the matching results to obtain the approximately optimal two-sided matching scheme. Finally, this paper verifies the correctness of the model and validity of the algorithm.

A competitive analysis of fail fast: Shakeout and uncertainty about consumer tastes

Contemporary business strategy advocates “fail fast”, the practice of launching a product early, before the resolution of uncertainty about how product characteristics matter to consumers. We consider how uncertainty about consumer preferences can contribute to shakeout when moving from the infant stage of an industry, when this uncertainty is large, to the mature stage of an industry, when this uncertainty is small. We find that, consistent with the empirical literature, due to firms’ uncertainty about consumers’ preferences, there is excessive entry initially and, on average, positive shakeout in the number of firms in the market in the mature phase of the industry. The paper also presents a new way to model uncertain preferences in product differentiation models that may prove useful in other applications.

A fuzzy goal programming with interval target model and its application to the decision problem of renewable energy planning

Optimizing sustainable renewable energy portfolios is one of the most complicated decision making problems in energy policy planning. This process involves meeting the decision maker’s preferences, which can be uncertain, while considering several conflicting criteria, such as environmental, societal, and economic impact. In this paper, rather than using existing techniques, a novel multi-objective decision making (MODM) model, named fuzzy goal programming with interval target (FGP-IT), is proposed and constructed based on recent developments and concepts in fuzzy goal programming (FGP) and revised multi-choice goal programming (RMCGP). The model deals with decision making problems involving a high level of uncertainty by offering decision makers a more flexible way to formulate and express their preferences, namely, fuzzy interval target goals. The proposed method is used to optimize a hypothetical sustainable wind energy portfolio in Algeria. The results show that the FGP-IT model is capable of assisting decision makers with uncertain preferences in making such complicated decisions.

New peer effect-based approach for service matching in Cloud Manufacturing under uncertain preferences

Cloud manufacturing is a kind of sharing manufacturing, and the supply–demand matching between manufacturing services and customers has become one of the most important issues for a platform. Because of the increasing complexity of customer personalization, the cognitive information from both sides becomes uncertain and fuzzy. Linguistics is used to describe uncertain preferences, especially in platforms. Also, the cloud model is adopted to convert the linguistics to reflect the randomness and fuzziness. Meanwhile, as the rapid development of communication techniques has strengthened the connections between different agents, the final matching results are affected by connections. Hence, the peer effect, which describes the mutual influences among individuals, is introduced in our study. In addition, considering the different strengths of the connections, the peer effect is improved by integrating grey relations, which are used to evaluate the diverse connections. Finally, the workload is introduced in the form of adjustment parameters. Consequently, we establish a bi-objective model that aims to maximize satisfaction and minimize the differences among individuals. To solve the mathematical model, an improved cuckoo algorithm is proposed. Additionally, the design of an air outlet grille for new-energy vehicles is taken as an example, and the effectiveness of the proposed method is verified.