An interval type-2 fuzzy sets based Delphi approach to evaluate site selection indicators of sustainable vehicle shredding facilities

This study aims to rank indicators affecting site selection of vehicle shredding facilities using an interval type-2 fuzzy sets based Delphi approach. The introduced methodology consists of four consecutive stages as follows: indicator identification, questionnaire (survey), decision-making analysis, and statistical analysis and indicator classification. In the first stage, the literature searches are performed on vehicle shredding facility location and forty-eight relevant indicators are determined. In the second stage, a questionnaire has been conducted to collect the preferences of relevant international experts from different countries regarding the indicators. Then, the importance of site selection indicators is obtained to define critical, medium, and uncritical indicators. In the last stage, the analysis are carried out to make a distinction between groups of participants who respond similarly and discover viewpoints from the industry and academia. The research findings show that the most important indicator for locating vehicle shredding facilities is a financial benefit. Critical indicators, which should be taken into account when locating vehicle shredding facilities, are acquisition cost, affected population, demand fluctuations, end-of-life vehicle policy, financial benefit, land availability, operational costs, recycling system, resource accessibility, and safety management

New complex proportional assessment approach using einstein aggregation operators and improved score function for interval-valued fermatean fuzzy sets

As a generalization of the Fermatean fuzzy set, the theory of interval-valued Fermatean fuzzy set (IVFFS) is a more robust and reliable tool to address the imprecise and incomplete information in the process of multi-criteria decision making (MCDM), thus can be employed on wider range of applications. The aim of this study is to purpose a novel decision-making approach by combining two well-recognized methods, named as the criteria interaction through inter-criteria correlation (CRITIC) and the complex proportional assessment (COPRAS) with IVFFSs. In this line, to compare the interval-valued Fermatean fuzzy numbers (IVFFNs), a new score function is proposed and its feasibility in comparison with existing interval-valued Fermatean fuzzy score and accuracy functions is discussed. To combine the various IVFFNs, some interval-valued Fermatean fuzzy Einstein aggregation operators are introduced. Further, the CRITIC is utilized to derive the objective weights of attributes within IVFFS context. To prioritize the alternatives, the IVFF-COPRAS method is presented on IVFFSs settings. Later, to assess the performance quality of the developed methodology, an illustrative case study is discussed to evaluate and rank the sustainable community-based tourism (CBT) location candidates. Moreover, the comparative study and sensitivity investigation are conducted to prove that the developed framework efficiently handles the problem of sustainable CBT locations evaluation and selection problem under IVFFSs environment. The findings of this study conclude that the developed method is a systematic, more comprehensive, accurate, and structured approach in the assessment of sustainable CBT locations under uncertain environment

A new hybrid fuzzy cybernetic analytic network process model to identify shared risks in PPP projects

A proper risk management strategy is essential in property management. For controlling and reducing risks on Public-Private Partnership (PPP) project, risk allocation is a major component of PPP risk management. Identifying appropriate shared risks and optimal risk allocation in a structured way is a complex process. The aim of this study is to develop a quantitative approach for equitable risk allocation with attention to identifying dependencies between risk allocation criteria and barriers. The paper presents an approach in the form of a hybrid Fuzzy method and Cybernetic Analytic Network Process (CANP) model for identifying shared risks. The approach involves the use of Fuzzy sets to convert linguistic principles and experiential expert knowledge into systematic quantitative analysis and the CANP to solve the problem of dependency and feedback between criteria and barriers as well as selection of shared risks. A case study is presented to demonstrate the use of the model in selecting shared risks. The study involves development of 10 criteria and 8 barriers. Finally, of 40 significant risks, 14 risks are successfully allocated between the public and private sector in Iranian PPP projects

A Decision Framework under a Linguistic Hesitant Fuzzy Set for Solving Multi-Criteria Group Decision Making Problems

With fast-growing interest in sustainable healthcare management, proper selection and evaluation of hospitals become highly essential. Generally, experts/decision-makers (DMs) prefer qualitative information for rating objects. Motivated by this idea, in this paper, a linguistic hesitant fuzzy set (LHFS) is adopted for elicitation of preference information. The LHFS provides qualitative preferences of DMs as well as reflects their hesitancy, inconsistency, and vagueness. Motivated by the power of LHFS, in this paper we present a new decision framework that initially presents some operational laws and properties. Further, a new aggregation operator called simple linguistic hesitant fuzzy weighted geometry (SLHFWG) is proposed under the LHFS context that uses the strength of power operators. Some properties of SLHFWG are also investigated. Criteria weights are estimated using a newly proposed linguistic hesitant fuzzy statistical variance (LHFSV) method, and objects are ranked using the newly proposed linguistic hesitant fuzzy VIKOR (visekriterijumska optimizacijai kompromisno resenje) (LHFVIKOR) method, which is an extension of VIKOR under the LHFS context. The practicality and usefulness of the proposal are demonstrated by using a hospital evaluation example for sustainable healthcare management. Finally, the strengths and weaknesses of the proposal are realized by comparison with other methods

Recent Fuzzy Generalisations of Rough Sets Theory: A Systematic Review and Methodological Critique of the Literature

Rough set theory has been used extensively in fields of complexity, cognitive sciences, and artificial intelligence, especially in numerous fields such as expert systems, knowledge discovery, information system, inductive reasoning, intelligent systems, data mining, pattern recognition, decision-making, and machine learning. Rough sets models, which have been recently proposed, are developed applying the different fuzzy generalisations. Currently, there is not a systematic literature review and classification of these new generalisations about rough set models. Therefore, in this review study, the attempt is made to provide a comprehensive systematic review of methodologies and applications of recent generalisations discussed in the area of fuzzy-rough set theory. On this subject, the Web of Science database has been chosen to select the relevant papers. Accordingly, the systematic and meta-analysis approach, which is called "PRISMA," has been proposed and the selected articles were classified based on the author and year of publication, author nationalities, application field, type of study, study category, study contribution, and journal in which the articles have appeared. Based on the results of this review, we found that there are many challenging issues related to the different application area of fuzzy-rough set theory which can motivate future research studies

Digitalization as a strategic means of achieving sustainable efficiencies in construction management: a critical review

Construction is a complex activity, characterized by high levels of capital investment, relatively long delivery durations, multitudinous risks and uncertainties, as well as requiring the integration of multiple skills delivering a huge volume of tasks and processes. All of these must be coordinated carefully if time, cost, and quality constraints are to be met. At the same time, construction is renowned for performing poorly regarding sustainability metrics. Construction activity generates high volumes of waste, requires vast amounts of resources and materials, while consuming a significant proportion of total energy generated. Digitalization of the construction workplace and construction activities has the potential of improving construction performance both in terms of business results as well as sustainability outcomes. This is because, to put it simply, reduced energy usage, for example, impacts economic and “green” performance, simultaneously. Firms tinkering with digitalization, however, do not always achieve the hoped-for outcomes. The challenge faced is that a digital transition of construction firms must be carried out at a strategic level—requiring a comprehensive change management protocol. What then does a digital strategy entail? This study puts forward an argument for the combined economic and sustainability dividends to be had from digitizing construction firm activities. It outlines the requirements for achieving digitalization. The elements of a comprehensive digitalization strategy are cataloged, while the various approaches to developing a digitalization strategy are discussed. This study offers practitioners a useful framework by which to consider their own firm-level efforts at digitalization transition

Outsourcing As A Measure Seeking For Cost Reduction In Public Health Care Sector: Lithuanian Case

Public institutions not much differ from big corporations in which both cost saving and efficiency are the main aims to consider the model of outsourcing. The object of this article is cost reduction in public health care sector. The aim of the article is to identify the factors that determine the choice of outsourcing as a measure to reduce costs in public health care sector. The results of the research revealed that non-core activities as well as investing in resources and resource warehousing are the factors that determine cost reduction in Lithuanian health care sector while such factors as customer loyalty development, customer attraction and administrative work do not earn much attention as the ones that enable to reduce costs while buying outsourcing services in public health care sector