A Likelihood-Based Qualitative Flexible Approach with Hesitant Fuzzy Linguistic Information

Application and Selection of Remediation Technology for OCPs-Contaminated Sites by Decision-Making Methods

The production and use of organochlorine pesticides (OCPs) for agricultural and industrial applications result in high levels of their residues, posing a significant risk to environmental and human health. At present, there are many techniques for OCP-contaminated soil remediation. However, the remediation of contaminated sites may suffer from a series of problems, such as a long recovery cycle, high costs, and secondary pollution, all of which could affect land redevelopment and reuse. Therefore, the selection of an appropriate technology is crucial for contaminated sites. In order to improve and support decision-making for the selection of remediation techniques, we provide a decision-making strategy for the screening of remediation techniques of OCP-contaminated sites. The screening procedure is proposed based on combining the analytic hierarchy process (AHP) and the technique for order preference by similarity to ideal solution (TOPSIS). The screening indexes include economic indicator, environmental indicator, and technical indicator. The assessment results show that co-processing in cement kiln obtained the highest overall score and was thus considered to be the most sustainable option. This suggested remediation technology was similar to the practical remediation project, indicating that the screening method could be applied for the selection of remediation technologies for sites contaminated with persistent organic pollutants.

Selecting the Optimal Mine Ventilation System via a Decision Making Framework under Hesitant Linguistic Environment

Ventilation systems are amongst the most essential components of a mine. As the indicators of ventilation systems are in general of ambiguity or uncertainty, the selection of ventilation systems can therefore be regarded as a complex fuzzy decision making problem. In order to solve such problems, a decision making framework based on a new concept, the hesitant linguistic preference relation (HLPR), is constructed. The basic elements in the HLPR are hesitant fuzzy linguistic numbers (HFLNs). At first, new operational laws and aggregation operators of HFLNs are defined to overcome the limitations in existing literature. Subsequently, a novel comparison method based on likelihood is proposed to obtain the order relationship of two HFLNs. Then, a likelihood-based consistency index is introduced to represent the difference between two hesitant linguistic preference relations (HLPRs). It is a new way to express the consistency degree for the reason that the traditional consistency indices are almost exclusively based on distance measures. Meanwhile, a consistency-improving model is suggested to attain acceptable consistent HLPRs. In addition, a method to receive reasonable ranking results from HLPRs with acceptable consistency is presented. At last, this method is used to pick out the best mine ventilation system under uncertain linguistic decision conditions. A comparison and a discussion are conducted to demonstrate the validity of the presented approach. The results show that the proposed method is effective for selecting the optimal mine ventilation system, and provides references for the construction and management of mines.

Graded Soft Expert Set as a Generalization of Hesitant Fuzzy Set

Hesitant fuzzy sets play a vital role in decision analysis. Although they have been proved to be a landmark in evaluating information, there are certain deficiencies in their structure. Also, in decision analysis with the aid of hesitant fuzzy sets, the relative importance of the decision makers according to their area of expertise is ignored completely, which may be misleading in some situations. These sorts of issues have been resolved in this work by using graded soft expert (GSE) sets. The proposed structure is a modified form of soft expert sets. Some basic operations have been introduced, and certain laws satisfied by them have carefully been investigated. With the aid of GSE sets, a decision-making algorithm (accompanied with an example) has been developed in which experts have been given due weightage according to their area of expertise.