Uncertain Information Environment Research Articles

Interval basic uncertain information and related aggregations in decision making

The recently proposed basic uncertain information can directly present numerical uncertainties for given real values, but it cannot handle given interval values which themselves also have uncertainties. Against this background, this work proposes the concept of interval basic uncertain information which serves as a generalization of basic uncertain information and involves two types of uncertainties. We analyze some basic operations, weighted arithmetic mean and preference transformation for interval basic uncertain information. The Rule-based decisions and the comprehensive certainty of interval basic uncertain information are also discussed. An illustrative example of multi-source multi-criteria evaluation under interval basic uncertain information environment is presented.

A New Multicriteria Decision-Making Method for the Selection of Sponge City Schemes with Shapley–Choquet Aggregation Operators

The construction of sponge cities is of great strategic significance to solving the urban water resource problem in the future. According to the policy guidance of sponge city construction, the evaluation index system of sponge city construction projects is constructed. In order to overcome the interference caused by the interaction between indexes, a nonadditive measure and Shapley function are combined to determine the weights of attribute indexes, and the generalized Shapley interval-valued intuitionistic uncertain linguistic Choquet averaging (GS-IVIULCA) operator is used to calculate the comprehensive evaluation value of the schemes. On this basis, a new evaluation method of sponge city construction project selection under an uncertain information environment is presented and empirically evaluated. The results show that the index weight of rainwater collection and utilization is the largest, indicating that decision makers pay more attention to the ecological and environmental benefits of this item in the sponge city construction process.

Basic uncertain information soft set and its application to multi-criteria group decision making

The purpose of this paper is to develop a novel type of soft set called basic uncertain information soft set (BUISS), which combines the notion of basic uncertain information and soft set theory. Since basic uncertain information can measure the quality of datum by using the degree of certainty, the developed BUISS provides a novel extension of soft set. In this paper, set operations and comparison law of basic uncertain information are also developed. Then, operations on BUISSs include set operations, ’AND’ operation and ’OR’ operation are defined. Besides, the Jaccard similarity between two BUISSs is introduced. The properties of the operations and similarity measure are studied. Theoretically, a multi-criteria group decision making (MAGDM) problem under basic uncertain information environment is analysed by using BUISSs. Two decision algorithms based on level BUISS and the aggregation of ϵ-approximate elements are proposed. Finally, an assessment of natural gas security in China is investigated to illustrate the feasibility and validity of the developed decision approaches.

Peer influence on trade credit

We examine the influence of peer firms on trade credit policies of listed firms in the United States. We posit and find evidence that firms mimic their peers in formulating trade credit policies. The findings are more pronounced for firms that operate in highly competitive product markets and an uncertain information environment. Our results show that firms not only mimic peers in similar circumstances but also imitate their more and less successful peers. We find that the benefits of mimicking peers' trade credit policies increase initially, but for firms that already maintain high levels of trade credit, these benefits diminish faster as the intensity of mimicking increases. Our results are robust to different methods of selecting peers, sampling, different proxies, and estimation techniques.

A multi criteria comprehensive evaluation approach for emergency response capacity with interval 2-tuple linguistic information

Emergency response capacity evaluation (ERCE) is an important and challenging issue in emergency management with the frequent occurrence of emergency events. Because of their different backgrounds and preferences, decision makers (DMs) often prefer to use linguistic variables from different linguistic term sets to express their decision information about the importance and the ratings of multi criteria involved in ERCE issue, some of which may be imprecise and uncertain. Therefore, this paper proposes a multi criteria comprehensive evaluation (MCCE) approach for ERCE with interval 2-tuple linguistic information. First, a new method for comparison between interval 2-tuples is proposed. Then, the classic analytical hierarchy process is extended into the ITL-AHP, in which the interval 2-tuple linguistic preference relation (ITLPR) and its multiplicative consistency are defined. An iterative algorithm to improve the consistency level of an ITLPR is proposed. Based on improved ITLPRs, the priority weights of criteria are derived by the normalizing rank summation method. In individual decision information collecting processes, both subjective weights and objective weights of DMs are considered. Furthermore, some interval 2-tuple interval weighted aggregation operators in which the weight vectors take the form of interval 2-tuple linguistic information are developed. Finally, a numerical illustration and comparison analyses are provided to elucidate the feasibility and superiority of the proposed method. The proposed method can provide a flexible way to perceive and appraise the emergency response capacity (REC) and an effective tool to handle the emergency management performance evaluation and decision making under fuzzy and uncertain information environment.

An integrated decision making approach for assessing healthcare waste treatment technologies from a multiple stakeholder

With increased worldwide awareness of environmental issues, healthcare waste (HCW) management has received much attention from both researchers and practitioners over the past decade. The task of selecting the optimum treatment technology for HCWs is a challenging decision making problem involving conflicting evaluation criteria and multiple stakeholders. In this paper, we develop an integrated decision making framework based on cloud model and MABAC method for evaluating and selecting the best HCW treatment technology from a multiple stakeholder perspective. The introduced framework deals with uncertain linguistic assessments of alternatives by using interval 2-tuple linguistic variables, determines decision makers’ relative weights based on the uncertainty and divergence degrees of every decision maker, and obtains the ranking of all HCW disposal alternatives with the aid of an extended MABAC method. Finally, an empirical example from Shanghai, China, is provided to illustrate the feasibility and effectiveness of the proposed approach. Results indicate that the methodology being proposed is more suitable and effective to handle the HCW treatment technology selection problem under vague and uncertain information environment.

Decision Method of Optimal Investment Enterprise Selection under Uncertain Information Environment

To select an optimal investment enterprise is the key to effectively reduce the investment risk for an investment company. In this paper, the author studies the problem of optimal investment enterprise selection decision under uncertain information environment (fuzzy information and grey information coexist), and present a fuzzy grey multi-attribute group decision making model to select the optimal investment enterprise. In this model, the author defines the concept and operations of fuzzy grey number, and present a ranking method based on fuzzy grey deviation degree to rank the alternative investment enterprises. The author also gives an application example of selecting optimal investment enterprise to highlight the implementation, availability, and feasibility of the proposed decision making model.

Group multi-criteria supplier selection using an extended VIKOR method with interval 2-tuple linguistic information

How to select the suitable suppliers in the supply chain is critical for an organization’s success and has attracted much attention of both researchers and practitioners. Supplier selection can be regarded as a complex group multiple criteria decision making problem requiring consideration of a number of alternative suppliers and quantitative and qualitative criteria. Additionally, decision makers cannot easily express their judgments on the alternatives with exact numerical values in many practical situations, and there usually exists uncertain and incomplete assessments. In response, this paper proposes an extended VIKOR method for group multi-criteria supplier selection with interval 2-tuple linguistic information. The feasibility and practicability of the proposed interval 2-tuple linguistic VIKOR (ITL-VIKOR) method are demonstrated through three realistic supplier selection examples and comparisons with the existing approaches. Results show that the ITL-VIKOR method being proposed is more suitable and effective to handle the supplier selection problem under vague, uncertain and incomplete information environment.

Application of interval 2-tuple linguistic MULTIMOORA method for health-care waste treatment technology evaluation and selection

The management of health-care waste (HCW) is a major challenge for municipalities, particularly in the cities of developing countries. Selection of the best treatment technology for HCW can be viewed as a complicated multi-criteria decision making (MCDM) problem which requires consideration of a number of alternatives and conflicting evaluation criteria. Additionally, decision makers often use different linguistic term sets to express their assessments because of their different backgrounds and preferences, some of which may be imprecise, uncertain and incomplete. In response, this paper proposes a modified MULTIMOORA method based on interval 2-tuple linguistic variables (named ITL-MULTIMOORA) for evaluating and selecting HCW treatment technologies. In particular, both subjective and objective importance coefficients of criteria are taken into consideration in the developed approach in order to conduct a more effective analysis. Finally, an empirical case study in Shanghai, the most crowded metropolis of China, is presented to demonstrate the proposed method, and results show that the proposed ITL-MULTIMOORA can solve the HCW treatment technology selection problem effectively under uncertain and incomplete information environment.

Optimal Model for Innovative Business Mode Selection of Cultural Industry under Uncertain Information Environment

In this paper, the problem of selecting the innovative business mode of cultural industry is studied, and is translated into a hybrid multi-attribute decision making with the values of attributes integrate with real numbers, interval numbers and fuzzy numbers. Concretely, the hybrid data is defined as the hybrid sequence, and the grey relational degree of hybrid sequences is given. Then an optimization model of selecting the optimal business mode is proposed based on grey relational degree of hybrid sequences. Finally, an application example is given to show how to apply this new model of innovative business mode selection.

Multi-UCAV Air Combat Task Assignment under Uncertain Information Environment

For UAV air combat task assignment problem, this paper presents an analysis method. Firstly, we analyzes the unmanned combat situation, establish the UAV air combat situation advantage function and interval information combat task allocation model. And then put forward multi-UAV air combat decision method of the discrete particle swarm optimization algorithm based on interval number sequence. Finally, we carry on the simulation, the simulation results show that the algorithm can effectively and reasonably solve the problem of multi-UAV air combat decision.

An interval 2-tuple linguistic MCDM method for robot evaluation and selection

Nowadays selection of an optimal robot has become a challenging task for manufacturers with the increment of production demands and availability of more different robot models. Robot selection for a particular industrial application can be viewed as a complicated multi-criteria decision-making problem which requires consideration of a number of alternative robots and conflicting subjective and objective criteria. Furthermore, decision-makers tend to use multigranularity linguistic term sets to express their assessments on the subjective criteria, and there usually exists uncertain and incomplete assessment information. In this paper, an interval 2-tuple linguistic TOPSIS (ITL-TOPSIS) method is proposed to handle the robot selection problem under uncertain and incomplete information environment. This method considers both subjective judgements and objective information in real-life applications, and models the uncertainty and diversity of decision-makers’ assessments using interval 2-tuple linguistic variables. An example is cited for demonstrating the feasibility and practicability of the proposed method, and results show that the ITL-TOPSIS is an effective decision-making tool for robot evaluation and selection with uncertain and incomplete information.

Multi-UCAV Task Allocation Based on SMAA in Uncertain Information Environment

According to the problem of multi-UCAV air-combat task allocation in uncertain environment, first consider the uncertainty elementsin the air-combat, the model of air-combat situation based on interval information is built, and combinat air combat situation predominance and attack gains, Finally, the model for task allocation of Multi-UCAV cooperative combat is built. Then Analysy to solve the problem used SMAA, the results show the feasibility and effectiveness of the method.

Supplier Selection Based on FAHP-VIKOR-IVIFs

As the most important part of supply chain management, supplier selection has always received a great deal of attention from practitioners and researchers. However, the nature of supplier selection is a complex multi-criteria decision making (MCDM) problem including many factors which may be in conflict. VIKOR is an efficient method to solve this kind of MCDM problem. But in reality, there usually exists incomplete and uncertain information, and the decision makers cannot easily express their judgments on the alternatives with exact and crisp values. Therefore, a new extended VIKOR method with interval-valued intuitionistic fuzzy sets (IVIF-VIKOR) is proposed in this paper. Then a hybrid MCDM method based on FAHP and the proposed IVIF-VIKOR are used to select supplier under incomplete and uncertain information environment. The feasibility and practicability of FAHP-IVIF-VIKOR method are further manifested through an empirical example.

Material selection using an interval 2-tuple linguistic VIKOR method considering subjective and objective weights

Nowadays, selection of an optimal material has become a challenging task for the designers in the design and development of products. Material selection for an engineering design can be viewed as a complicated multi-criteria decision making (MCDM) problem which requires consideration of a number of available materials and conflicting tangible and intangible selection criteria. Moreover, decision makers tend to use multigranularity linguistic term sets for expressing their assessments because of their different backgrounds and preferences, some of which may be uncertain and incomplete. Therefore, this paper presents an interval 2-tuple linguistic VIKOR (ITL-VIKOR) method for solving the material selection problem under uncertain and incomplete information environment. The new method can not only model the uncertainty and diversity of decision makers’ assessment information but also consider the subjective and objective weights of criteria simultaneously in the material selection process. The method has exact characteristic and can avoid information distortion and losing in the linguistic information processing. Finally, two examples are included to demonstrate the feasibility and practicability of the proposed method.

A Multi-Criteria Intuitionistic Fuzzy Group Decision Making Method for Supplier Selection with VIKOR Method

Supplier selection, the process of finding the right suppliers who are able to provide the buyer with the right quality products and/or services at the right price, at the right time and in the right quantities, is one of the most critical activities for establishing an effective supply chain, and is typically a multi-criteria group decision problem. In many practical situations, there usually exists incomplete and uncertain information, and the decision makers cannot easily express their judgments on the candidates with exact and crisp values. Therefore, in this paper an extended VIKOR method for group decision making with intuitionistic fuzzy numbers is proposed to solve the supplier selection problem under incomplete and uncertain information environment. In other researches in this area, the weights of each decision makers and in many of them the weights of criteria are pre-determined, but these weights have been calculated in this paper by using the decision matrix of each decision maker. Also, normalized Hamming distance is proposed to calculate the distance between intuitionistic fuzzy numbers. Finally, a numerical example for supplier selection is given to clarify the main results developed in this paper.

Group Decision Making Process for Supplier Selection with TOPSIS Method under Interval-Valued Intuitionistic Fuzzy Numbers

Supplier selection is a fundamental issue of supply chain area that heavily contributes to the overall supply chain performance, and, also, it is a hard problem since supplier selection is typically a multicriteria group decision problem. In many practical situations, there usually exists incomplete and uncertain, and the decision makers cannot easily express their judgments on the candidates with exact and crisp values. Therefore, in this paper an extended technique for order preference by similarity to ideal solution (TOPSIS) method for group decision making with Atanassov's interval-valued intuitionistic fuzzy numbers is proposed to solve the supplier selection problem under incomplete and uncertain information environment. In other researches in this area, the weights of each decision maker and in many of them the weights of criteria are predetermined, but these weights have been calculated in this paper by using the decision matrix of each decision maker. Also, the normalized Hamming distance is proposed to calculate the distance between Atanassov's interval-valued intuitionistic fuzzy numbers. Finally, a numerical example for supplier selection is given to clarify the main results developed in this paper.

An extended TOPSIS method with interval-valued intuitionistic fuzzy numbers for virtual enterprise partner selection

How to select suitable partners is critical to virtual enterprise (VE) success and has attracted much attention for both researchers and practitioners. In many practical situations, there usually exists incomplete and uncertain information, and the decision makers cannot easily express their judgments on the candidates with exact and crisp values. Therefore, an extended technique for order preference by similarity to ideal solution (TOPSIS) method for group decision making with interval-valued intuitionistic fuzzy numbers is proposed to solve the partner selection problem under incomplete and uncertain information environment in this paper. And the feasibility and practicability of the extended TOPSIS method are further manifested through an illustrative example. Results show that this extended TOPSIS method for group decision making with interval-valued intuitionistic fuzzy numbers is more suitable to cope with the partner selection problem under incomplete and uncertain information environment.

A New Maximum Entropy Estimation of Distribution Algorithm to Solve Uncertain Information Job-shop Scheduling Problem

Estimation of Distribution Algorithm (EDA) is a new kinds of colony evolution algorithm, through counting excellent information of individuals of the present colony EDA construct probability distribution model, then sample the model produces newt generation. To solve the NP-Hard question as EDA searching optimum network structure a new Maximum Entropy Distribution Algorithm (MEEDA) is provided. The algorithm takes Jaynes principle as the basis, makes use of the maximum entropy of random variables to estimate the minimum bias probability distribution of random variables, and then regard it as the evolution model of the algorithm, which produces the optimal/near optimal solution. Then this paper presents a rough programming model for job shop scheduling under uncertain information problem. The method overcomes the defects of traditional methods which need pre-set authorized characteristics or amount described attributes, designs multi-objective optimization mechanism and expands the application space of a rough set in the issue of job shop scheduling under uncertain information environment. Due to the complexity of the proposed model, traditional algorithms have low capability in producing a feasible solution. We use MEEDA in order to enable a definition of a solution within a reasonable amount of time. We assume that machine flexibility in processing operations to decrease the complexity of the proposed model. Muth and Thompson's benchmark problems tests are used to verify and validate the proposed rough programming model and its algorithm. The computational results obtained by MEEDA are compared with GA. The compared results prove the effectiveness of MEEDA in the job shop scheduling problem under uncertain information environment.

A comprehensive model for selecting information system project under fuzzy environment

In general, the development of any information system (IS) project requires large investments of resources, such as human resources, computer software and hardware resources, operational procedures adjustments, and so on. However, IS project selection is difficult because there are lots of factors to be considered, such as business goals, benefits, project risks and limited available resources. In fact, IS project selection takes place in an incomplete, vague and uncertain information environment. The aim of this paper is to present a multiple-criteria decision-making method (MCDM) for selecting an information system project based on the fuzzy measure and the fuzzy integral. In this paper, the subjective opinions of decision makers are described in linguistic terms expressed in trapezoidal fuzzy numbers. After aggregating the fuzzy ratings of all decision makers, the vertex method is applied to transform the aggregated fuzzy rating into a crisp value. And then, a new algorithm is developed to deal with the IS projects selection problems. Finally, a numerical example is given to demonstrate the procedure for the proposed method.