Abstract: Abstract Evaluating and selecting suitable sustainable recycling partners is a key work in sustainable supply chain management. In order to deal with the probabilistic linguistic influence relations between criteria and obtain the key factors that influence the evaluation results of sustainable recycling partners, we propose a new decision-making trial and evaluation laboratory (DEMATEL) method. First, we propose a new generalized weighted ordered weighted averaging (GWOWA) operator and discuss its properties. Second, we use probabilistic linguistic term sets (PLTSs) to aggregate the experts’ hesitant fuzzy linguistic decision-making information and develop a novel method of transforming PLTSs into triangular fuzzy numbers (TFNs) based on the proposed GWOWA operator and the characteristics of PLTSs. Furthermore, we propose a method of making criteria relation analysis based on DEMATEL with TFNs. With the method, we not only access the importance weights of criteria but also obtain the influence relation among the criteria and cluster the criteria into two groups: cause group and effect group. Finally, we apply our method to a real case of sustainable recycling partner selection. PubDate: 2020-12-01
Abstract: Abstract The aim of the paper is to solve the group decision making problems which contain inconsistent probabilistic linguistic preference relations (PLPRs) and unknown expert weights. When the PLPRs are inconsistent, there are contradictories in the preference relations expressed by the experts. The evaluation value with contradictory information will bring out an incorrect consequence in decision making. Hence, this paper develops a novel consistency measure method to gauge the consistency level of PLPRs. Moreover, a nonlinear optimization model is newly constructed to optimize the inconsistent PLPRs. The proposed methods overcome the limitations in the existing methods and ameliorate the interpretation and complexities of inconsistency PLPRs revise strategies. Additionally, a weighting method using fuzzy cooperative games with PLPRs is put forward to derive the weight vector of experts. It helps to balance the deviations between the individual PLPRs and the group PLRP. At last, a numerical example illustration for physician selection is put forward to demonstrate the effectiveness of the proposed model and its practical applicability. The comparative analysis gives deep insights into the rationality of the proposed model. PubDate: 2020-12-01
Abstract: Abstract As a representative of the new economy, the web celebrity economy has achieved significant development in China with the rapid development of information technology and the Internet. In this environment, web celebrity shops encounter fierce business competition of peer competitors. Online reviews which imply the consumers’ attitudes and sentiments give the web celebrity shops good feedback to improve their competitiveness. Thus, taking milk tea as an example, this paper deeply investigates the assessment of web celebrity shops by mining online review. At the same time, we also discuss the competitive analysis and propose the corresponding improvement advices. In order to obtain the satisfaction assessments of web celebrity shops, on the one hand, we analyze topic extraction with latent dirichlet allocation (LDA) and determine the attributes that customers care about. On the other hand, we utilize long short-term memory (LSTM) and probabilistic linguistic term sets (PLTSs) to more precisely portray customers’ sentiment towards different attributes. By using fuzzy cognitive map (FCM) and the association rule, we further investigate the interrelationship among the attributes and construct the relationship graph between attributes for web celebrity shops. With the above results, we aggregate the decision information by designing improved extended Bonferroni mean (EBM) and obtain comprehensive evaluations. General speaking, this paper successfully transforms the unstructured data of online reviews into quantitative information and obtain satisfaction evaluations. With the aid of PLTSs and FCM, we further investigate the competitive analysis and propose improvement advices for each shop, which systematically provides us with a data-driven decision-making analysis model. PubDate: 2020-12-01
Abstract: Abstract In this paper, we study four projection-based normalization models and a decision-making method for probabilistic linguistic multi-criteria decision-making problems, in which the assessment information about an alternative with respect to a criterion is incomplete and the criteria weight values are not precisely known but the ranges are available. To apply the projection to the probabilistic linguistic environment, we propose the equivalent expression forms of the probabilistic linguistic term sets, and then the equivalent transformation functions between the probabilistic linguistic term set and its associated vector are presented to realize the conversion between the operations on the probabilistic linguistic term sets and the operations on their associated vectors. Next, the projection formulas of the probabilistic linguistic term sets are introduced to build different normalization models for different types of uncertain probabilistic linguistic multi-criteria decision-making problems. After that, a new deviation degree formula is proposed to account for the rationality and validity of the normalization models from the theoretical perspective. Finally, the probabilistic linguistic two-step method is used to determine the criteria weights values and rank the alternatives, and the validity of these projection-based normalization models and our proposed decision-making method are illustrated by a case about the performance assessment of data hiding techniques. PubDate: 2020-12-01
Abstract: Abstract When expressing preferences with different probability weights for different linguistic terms, only partial assessment information is usually to be provided. Then the probability information can be normalized to the interval probability, hence, using interval probabilistic linguistic term sets (IPLTs) is more appropriate. Considering this situation, interval probabilistic linguistic preference relation (IPLPR) is proposed. To measure the consistency of IPLPR, the consistency definition of IPLPR is put forward. For the consistent IPLPR, from which an expected consistent PLPR can be obtained, we can obtain interval weights as the final priorities by using the pairs of linear programming models. We also create the probabilistic linguistic geometric consistency index (PLGCI) of PLPRs to judge whether the IPLPR is satisfactorily consistent. For an unsatisfied consistency IPLPR, the adjusting algorithm is proposed. Probability information is firstly considered to be adjusted. If it is not possible to achieve satisfactory consistency through the adjustment of probability information, then the linguistic terms will be adjusted. In addition to examples of different situations, such as the consistency, satisfactory consistency and consistency improvement, the application example is also given to show the practicability of the proposed methods. PubDate: 2020-12-01
Abstract: Abstract The probabilistic linguistic term set (PLTS) is a powerful tool for describing linguistic evaluations derived from expert teams and has adequate capability to identify preferences among different evaluations. Due to the practicability of PLTSs, probabilistic linguistic decision making problems have been widely investigated in recent years. However, no study on probabilistic linguistic outranking relations has been conducted. This study aims to explore effective processing for the complex two-dimension structure of PLTSs and formulate probabilistic linguistic dominance and opposition relations for multi-criteria sorting decision making. Linguistic scale functions, which can generate different semantics for linguistic variables under different decision making environments, are introduced to deal with the linguistic terms in PLTSs. In this way, the probabilistic linguistic dominance degree, concordance and discordance indices are defined by systematically comparing the probabilities of PLTSs. Then, two kinds of outranking relations with dominance and opposition for PLTSs are formulated based on the defined outranking indices. Subsequently, an innovative sorting decision making framework is constructed by exploring the outranking relations between alternatives and characteristic actions under multiple criteria and implementing the outranking aggregation and exploitation. Finally, this framework is demonstrated using an illustrative example with result analyses and comparison discussions. PubDate: 2020-12-01
Abstract: Abstract Selecting financial products is one of the most fundamental investment activities to both individuals and companies, and therefore it is very important to establish an efficient and practical method for financial products selection. To address the challenge of the complicated decision-making environment and decision makers’ expression habits for the selection of financial products, this paper develops the incomplete additive probabilistic linguistic preference relation to depict decision makers’ preferences. Considering that, when decision makers express their opinions using probabilistic linguistic preference relation, it is possible that the sum of the value of the probability information is more than 1, this paper also extends the concepts of probabilistic linguistic term set, additive probabilistic linguistic preference relation and incomplete additive probabilistic linguistic preference relation to improve and ensure their practicability. Moreover, an “inverse prospect theory-based” algorithm is proposed to choose proper financial products. The algorithm processes the original incomplete additive probabilistic linguistic preference relation by using the inverse functions of the prospect theory at first. Then, a priority weights deriving model is established based on the extended concepts. Finally, the numerical case and analysis is presented as the evidences to the conclusion that the proposed algorithm is practical and robust. PubDate: 2020-11-05
Abstract: Abstract Quality function deployment (QFD) is a customer-oriented product/service design tool with diversified team members reaching a consensus in developing a new or an improved product/service to maximize customer satisfaction. Determining the relative importance ratings (RIRs) of customer requirements (CRs) is actually a multiple attribute decision making (MADM) problem, which is regarded as an essential step in QFD application. Although many decision making approaches have been developed to rate the CRs, few of them refer to derive the weights of evaluators, and there is paucity of literature which combines the probabilistic linguistic term sets (PLTSs) with QFD methodology. Therefore, PLTSs are introduced in our study, to simultaneously reflect the hesitancy and preference degrees of evaluators. A novel MADM technique based on probabilistic linguistic preference (PLP) is explored to calculate the RIRs among CRs under the PLP environment. Concretely, some operators, such as the normalization formulas, the probabilistic linguistic expected value (PLEV) operator as well as the standard formula, are applied to weight the decision makers; a modified grey relational analysis-technique for order preference by similarity to ideal solution (GRA-TOPSIS) method called probabilistic linguistic-based GRA-TOPSIS is also proposed to rate the relative importance over CRs. Finally, application of product improvement of a turbine engine is given to see the validity and feasibility of the proposed approach. PubDate: 2020-10-26
Abstract: Abstract The Evidential C-Means algorithm provides a global treatment of ambiguity and uncertainty in memberships when partitioning attribute data, but still requires the number of clusters to be fixed as a priori, like most existing clustering methods do. However, the users usually do not know the exact number of clusters in advance, particularly in practical engineering. To relax this requirement, this paper proposes an Evidential Evolving C-Means (E2CM) clustering method in the framework of evolutionary computation: cluster centers are encoded in a population of variable strings (or particles) to search the optimal number and locations of clusters simultaneously. To perform such joint optimization problem well, an artificial bee colony algorithm with variable strings and interactive evaluation mode is proposed. It will be shown that the E2CM can automatically create appropriate credal partitions by just requiring an upper bound of the cluster number rather than the exact one. More interestingly, there are no restrictions on this upper bound from the theoretic point of view. Some numerical experiments and a practical application in thermal power engineering validate our conclusions. PubDate: 2020-10-06
Abstract: Abstract The capacitated p-center problem is concerned with how to select p locations for facility centers and assign demand points to them such that the maximum distance between a demand point and its nearest center is minimized. This paper focuses on the capacitated p-center problem in an uncertain environment, in which demands and distances are regarded as uncertain variables. Consequently, two minimax models with uncertain parameters are formulated, and their crisp equivalences are investigated. Additionally, a hybrid algorithm based on the 99-method, a genetic algorithm and a tabu search algorithm is designed to solve the models. Finally, some numerical examples are presented to unveil the applications of the models and algorithm. PubDate: 2020-10-02
Abstract: Abstract Optimal control problems governed by two different types of uncertain discrete-time singular systems are investigated under expected value criterion. The objective function including uncertain variables is optimized with the help of expected value method provided that the singular systems are both regular and impulse-free. At first, based on the principle of dynamic programming, a recurrence equation is derived to simplify an optimal control model for a class of uncertain discrete-time singular systems. After that, according to uncertainty theory and the recurrence equation, two kinds of optimal control problems subject to an uncertain linear singular system and an uncertain singular system with quadratic input variables are considered in order, and the optimal solutions are both presented by accurate expressions. A numerical example and a dynamic input-output model are settled to illustrate the effectiveness of the results obtained. PubDate: 2020-10-02
Abstract: Abstract This paper studies comparative static effects in a portfolio selection problem when the investor has mean-variance preferences. Since the security market is complex, there exists the situation where security returns are given by experts’ estimates when they cannot be reflected by historical data. This paper discusses the problem in such a situation. Based on uncertainty theory, the paper first establishes an uncertain mean-variance utility model, in which security returns and background asset returns are uncertain variables and subject to normal uncertainty distributions. Then, the effects of changes in mean and standard deviation of uncertain background asset on capital allocation are discussed. Furthermore, the influence of initial proportion in background asset on portfolio investment decisions is analyzed when investors have quadratic mean-variance utility function. Finally, the economic analysis illustration of investment strategy is presented. PubDate: 2020-09-30
Abstract: Abstract The Susceptible-Exposed-Infectious-Asymptomatic-Removed (SEIAR) epidemic model is one of most frequently used epidemic models. As an application of uncertain differential equations to epidemiology, an uncertain SEIAR model is derived which considers the human uncertainty factors during the spread of an epidemic. The parameters in the uncertain epidemic model are estimated with the numbers of COVID-19 cases in China, and a prediction to the possible numbers of active cases is made based on the estimates. PubDate: 2020-09-24
Abstract: Abstract As a type of coronavirus, COVID-19 has quickly spread around the majority of countries worldwide, and seriously threatens human health and security. This paper aims to depict cumulative numbers of COVID-19 infections in China using the growth model chosen by cross validation. The residual plot does not look like a null plot, so we can not find a distribution function for the disturbance term that is close enough to the true frequency. Therefore, the disturbance term can not be characterized as random variables, and stochastic regression analysis is invalid in this case. To better describe this pandemic automatically, this paper first employs uncertain growth models with the help of uncertain hypothesis tests to detect and modify outliers in data. The forecast value and confidence interval for the cumulative number of COVID-19 infections in China are provided. PubDate: 2020-09-19
Abstract: Abstract Developing algorithms for solving high-dimensional uncertain differential equations has been an exceedingly difficult task. This paper presents an \(\alpha \) -path-based approach that can handle the proposed high-dimensional uncertain SIR model. We apply the \(\alpha \) -path-based approach to calculating the uncertainty distributions and related expected values of the solutions. Furthermore, we employ the method of moments to estimate parameters and design a numerical algorithm to solve them. This model is applied to describing the development trend of COVID-19 using infected and recovered data of Hubei province. The results indicate that lockdown policy achieves almost 100% efficiency after February 13, 2020, which is consistent with the existing literatures. The high-dimensional \(\alpha \) -path-based approach opens up new possibilities in solving high-dimensional uncertain differential equations and new applications. PubDate: 2020-09-17
Abstract: Abstract This paper presents an uncertain time series model to analyse and predict the evolution of confirmed COVID-19 cases in China, excluding imported cases. Compared with the results of the classical time series model, the uncertain time series model could better describe the COVID-19 epidemic by using an uncertain hypothesis test to filter out outliers. This improvement is reflected in the two observations. One is that the estimated variance of the disturbance term in the uncertain time series model is more appropriate and acceptable than that in the classical time series model, and the other is that the disturbance term of the classical time series model cannot be regarded as a random variable but as an uncertain variable. PubDate: 2020-09-16
Abstract: Abstract This paper finds a relation between moments of Liu process and Bernoulli numbers. Firstly, by an exponential generating function of Bernoulli numbers, a useful integral formula is obtained. Secondly, based on this integral formula, the moments of a normal uncertain variable and Liu process are expressed via Bernoulli numbers. PubDate: 2020-09-16
Abstract: Abstract Assume an uncertain process follows an uncertain differential equation, and some realizations of this process are observed. Parameter estimation for the uncertain differential equation that fits the observed data as much as possible is a core problem in practice. This paper first presents a problem of initial value estimation for uncertain differential equations and proposes an estimation method. In addition, the method of moments is recast for estimating the time-varying parameters in uncertain differential equations. Using those techniques, a COVID-19 spread model based on uncertain differential equation is derived, and the zero-day of COVID-19 spread in China is inferred. PubDate: 2020-09-15
Abstract: Abstract Smart manufacturing is an effective way to improve the efficiency of resource utilization and reduce the response time of making joint decisions for the enterprises. Though, with the globalization of manufacturing enterprises, manufacturing optimization problems often occur in complex manufacturing systems under the deteriorating and fuzzy environment, which brings many challenges to smart manufacturing, such as the lack of coordinating scheduling strategies to guarantee the low latency requirement. This paper investigates a robust parallel-batching scheduling problem with fuzzy processing time and past-sequence-dependent delivery time. Some structural properties are first identified, and an optimal algorithm is further developed for the single-machine scheduling problem. Then, the problem is proved to be NP-hard. We thus design a hybrid Multi-Verse Optimizer-Variable Neighborhood Search algorithm to solve the investigated problem in a reasonable time. Abundant experiments of different scales are conducted to verify the performance of the proposed hybrid method with a comparison of the state-of-the-art methods. The proposed hybrid meta-heuristic shows excellent results, robustness, and computational time performance under various experiments. PubDate: 2020-04-03
Hybrid journal (It can contain Open Access articles)
