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Interval-Valued Pythagorean Fuzzy Sets (IVPFSs) as an enhanced type of Pythagorean Fuzzy Sets (PFSs) improve the expression of membership, non-membership, and hesitancy degrees, compared to Intuitionistic Fuzzy Sets (IFSs). In order to benefit from the advantages of IVPFSs, the current research proposes a new group decisionmaking method based on Linear Assignment Method (LAM). In this approach, both subjective and objective weights of criteria were taken into account. Moreover, the proposed method applied a new ranking method for IVPFSs. To avoid the shortcomings of the first aggregation methods, the introduced decision-making approach focused on the last aggregation approach. Finally, the method was used in a case study of sustainable project evaluation in order to depict the applicability of this method. [ABSTRACT FROM AUTHOR]

Different maintenance policies including Preventive Maintenance (PvM) and Predictive Maintenance (PdM) have been introduced to enhance the operation of systems. Maintenance professional experts have faced numerous challenges in distinguishing between proper maintenance policies among which causes of failure, accessibility, and capability of maintenance should be regarded seriously. Moreover, most organizations do not have a deliberate and compelling model for evaluating maintenance policies under uncertainty to deal with real-world conditions. The aim of this paper is to introduce a new Interval-Valued Fuzzy (IVF) decision model to select a maintenance policy based on order inclination with comparability to ideal solutions through Monte Carlo simulation. This paper introduces novel separation measures and a new IVF-distinguished index based on Possibilistic Statistical Concepts (PSCs) so that maintenance Decision-Makers (DMs) feel aided in ranking maintenance policy candidates. Also, Resilience Engineering (RE) factors are considered based on conventional evaluation criteria. Finally, the steps of the proposed IVF model-based PSCs are applied to survey a real case in the manufacturing industry. Results of the presented model are compared with those existing in the recent literature and the outcome could help maintenance personnel in identifying the best policy systematically. [ABSTRACT FROM AUTHOR]

Project management is a process that schemes and controls the project life cycle via the easiest and the best way to achieve project goals. Project managers always aim to simultaneously handle conflicting goals in the organization. In this paper, a new mathematical model is proposed that simultaneously minimizes total cost and completion time and maximizes the quality in the project management decision problem. Contractual penalty cost and contractual reward cost with a new method are the other consideration in the proposed model. In the projects, the relation between time and direct cost is a nonlinear function. Hence, a linearization technique is presented with attention to variable change and piecewise linearization, in which nonlinear function is converted to the linear programming model. On the other hand, in real conditions according to uncertainty in environmental situations and incomplete information, there can be ambiguity in parameters and variables of the problem. The uncertainty of the parameters and variables is expressed with fuzzy sets theory and fuzzy mathematical programming. The other aim of this paper is to introduce a modified version of fully fuzzy multi-objective linear programming for the problem. For analyzing a fully fuzzy time–cost–quality project management model, a practical example of the literature is provided. By examining the results of the model with conflicting objectives, two scenarios are presented to explore the interactions of conflicting objectives on the project, and the results are reported. [ABSTRACT FROM AUTHOR]

High-technology projects are known as tools that help achieving productive forces through scientific and technological knowledge. These knowledge-based projects are associated with high levels of risks and returns. The process of hightechnology project and project portfolio selection has technical complexities and uncertainties. This paper presents a novel two-parted method of high-technology project portfolio selection. In the first part, a new decision-making model under Pythagorean fuzzy set (PFS) uncertainty is introduced that is last aggregation and avoids defuzziffcation until the last step of the process. In the last step, a new PFS ranking method is used to make crisp and comparable values. Outcomes from this part form the objective function of a new integer programming (IP) of the project portfolio selection. To display the models application, data from a real case study of high-technology project evaluation and selection is presented, and the steps of the approach are illustrated in addition to presenting the effcacy of the model. [ABSTRACT FROM AUTHOR]

Recently, sustainable warehouse location has been regarded as one of the most critical and significant decision problems for long-term planning in the supply chain. This strategic decision can be effected by different quantitative and qualitative evaluation criteria via three dimensions of the sustainability. Main theme of the paper is to select the most optimal location decision from a number of potential sustainable warehouse candidates. For this purpose, this paper presents a novel multi-criteria decision-making model by a group of supply chain experts or decision makers with interval-valued fuzzy setting and asymmetric uncertainty information. Concepts of mean, variance and skewness are introduced into the proposed group decision model, and their mathematical relations are defined based on a fuzzy possibilistic statistical approach. Then, new relations in this model are presented for obtaining ideal solutions under uncertainty with two high and low values of the possibilistic mean and possibilistic standard deviation, along with the possibilistic cube root of skewness. In addition, novel separation measures and new fuzzy ranking index of hybridized relative closeness coefficients are presented to provide final preference order of warehouse location candidates under uncertain conditions. Finally, a sustainable warehouse location selection problem in a pharmaceutical company is presented and solved by the proposed group decision model to demonstrate its applicability and suitability. [ABSTRACT FROM AUTHOR]

Failure modes and effects analysis (FMEA) is a well-known risk analysis approach that has been conducted to distinguish, analyze and mitigate serious failure modes. It demonstrates the effectiveness and the ability of understanding and documenting in a clear manner; however, the FMEA has weak points and it has been criticized by some authors. For example, it does not consider relative importance among three risk factors (i.e., O, S and D). Different sequences of O, S and D may result in exactly the same value of risk priority number (RPN), but their semantic risk implications may be totally different and these three risk factors are difficult to be precisely expressed. This study introduces a new interval-valued intuitionistic fuzzy (IVIF)-decision approach based on compromise solution concept that defeats the above weak points and improves the traditional FMEA’s results. This study firstly employs both subjective and objective weights in the decision process simultaneously. Secondly, there are two kinds of subjective weights performed in the study: aggregated weights obtained by experts’ assessments as well as entropy measure. Thirdly, this approach is defined under an IVIF-environment to ensure that the evaluation information would be preserved, and the uncertainties could be handled during the computations. Hence, it considers uncertainty in experts’ judgments as well as reduces the probability of obtaining two ranking orders with the same value. Finally, the alternatives are ranked with a new collective index according to the compromise solution concept. To show the effectiveness of the proposed approach, two practical examples are solved from the recent literature in engineering applications. The proposed decision approach has an acceptable performance. Also, its advantages have been mentioned in comparison with other decision approaches. [ABSTRACT FROM AUTHOR]

Organizations need to evaluate project proposals and select the ones that are the most effective in reaching the strategic goals by considering sustainability issue. In order to enhance the effectiveness and the efficiency of project oriented organizations, in this paper a new multi-objective decision making (MODM) approach of sustainable project portfolio selection is proposed which applies interval-valued fuzzy sets (IVFSs) to consider uncertainty. In the proposed approach, in addition to sustainability criteria, other practical criteria including non-financial benefits, strategic alignment, organizational readiness and project risk are incorporated. The presented approach consists of three main parts: In the first part, a novel composite risk re turn index based on the IVFSs is introduced and used to form the first model to evaluate the financial re turn and risk of the proposed projects. In the second p a rt, a new risk reduction compromise ratio model is introduced to evaluate projects versus non-financial criteria. Finally, an MODM model is presented to form the overall objective function of the approach. In order to make the approach more suitable for real-world situations, a group of applicable constraints is included in the proposed approach. The constraints are based on limitations and issues existing in practical project portfolio management. Due to importance of uncertainty and risk in project portfolio selection, they are addressed separately in three parts of the approach. In the first part, a novel downside risk measure is introduced and applied to assess financial risk of projects. In the second part of the approach, not only project risk is accounted for as a criterion, bu t also a new method is introduced to control and limit the risk of uncertainty and to use the advantages of IVFSs. Finally, the proposed IVF-MODM approach is applied to select the optimal sustainable project portfolio in real case study of a holding company in a developing country. The results show that the approach can successfully address highly uncertain environments. Moreover, risk has been fully explored from different perspectives Eventually, the approach provided the decision makers with more flexibility in focusing on financial and non-financial criteria in the selection process. [ABSTRACT FROM AUTHOR]

Multiple attributes group decision making (MAGDM) is regarded as the process of determining the best feasible solution by a group of experts or decision makers according to the attributes that represent different effects. In assessing the performance of each alternative with respect to each attribute and the relative importance of the selected attributes, quantitative/qualitative evaluations are often required to handle uncertainty, imprecise and inadequate information, which are well suited to represent with fuzzy values. This paper develops a VIKOR method based on intuitionistic fuzzy sets with multi-judges and multi-attributes in real-life situations. Intuitionistic fuzzy weighted averaging (IFWA) operator is used to aggregate individual judgments of experts to rate the importance of attributes and alternatives. Then, an intuitionistic ranking index is introduced to obtain a compromise solution to solve MAGDM problems. For application and validation, this paper presents two application examples and solves the practical portfolio selection and material handling selection problems to verify the proposed method. Finally, the intuitionistic fuzzy VIKOR method is compared with the existing intuitionistic fuzzy MAGDM method for two application examples, and their computational results are discussed. [ABSTRACT FROM AUTHOR]

This paper proposes a model on the basis of VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methodology as a compromised method to solve the Multi-Objective Large-Scale Nonlinear Programming (MOLSNLP) problems with block angular structure involving fuzzy coefficients. The proposed method is introduced for solving large scale nonlinear programming in fuzzy environment for first time. The problem involves fuzzy coefficients in both objective functions and constraints. In this method, an aggregating function developed from LP-metric is based on the particular measure of "closeness" to the "ideal" solution. The solution process is composed of two steps: First, the decomposition algorithm is utilized to reduce the q-dimensional objective space into a one-dimensional space. Then a multi-objective identical crisp non-linear programming is derived from each fuzzy non-linear model for solving the problem. Second, for finding the final solution, a single-objective large-scale nonlinear programming problem is solved. In order to justify the proposed method, an illustrative example is presented and followed by description of the sensitivity analysis. [ABSTRACT FROM AUTHOR]