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The aim of this study as to find out an approach for emergency program selection.

The authors have generated six aggregation operators (AOs), namely picture fuzzy Yager weighted average (PFYWA), picture fuzzy Yager ordered weighted average, picture fuzzy Yager hybrid weighted average, picture fuzzy Yager weighted geometric (PFYWG), picture fuzzy Yager ordered weighted geometric and picture fuzzy Yager hybrid weighted geometric aggregations operators.

First of all, the authors defined the score and accuracy function for picture fuzzy set (FS), and some fundamental operational laws for picture FS using the Yager aggregation operation. After that, using the developed operational laws, developed some AOs, namely PFYWA, picture fuzzy Yager ordered weighted average, picture fuzzy Yager hybrid weighted average, PFYWG, picture fuzzy Yager ordered weighted geometric and picture fuzzy Yager hybrid weighted geometric aggregations operators, have been proposed along with their desirable properties. A decision-making (DM) approach based on these operators has also been presented. An illustrative example has been given for demonstrating the approach. Finally, discussed the comparison of the proposed method with the other existing methods and write the conclusion of the article.

To find the best alternative for emergency program selection.

Risk assessment is a very important step toward managing risks in various organizations and industries. One of the most extensively applied risk assessment techniques is failure mode and effects analysis (FMEA). In this paper, a novel fuzzy multiple-criteria decision-making (MCDM)-based FMEA model is proposed for assessing the risks of different failure modes more accurately.

In this model, the weight of each failure mode is considered instead of risk priority number (RPN). Additionally, three criteria of time, cost and profit are added to the three previous risk factors of occurrence (O), severity (S) and detection (D). Furthermore, the weights of the mentioned criteria and the priority weights of the decision-makers calculated by modified fuzzy AHP and fuzzy weighted MULTIMOORA methods, respectively, are considered in the proposed model. A new ranking method of fuzzy numbers is also utilized in both proposed fuzzy MCDM methods.

To show the capability and usefulness of the suggested fuzzy MCDM-based FMEA model, Kerman Steel Industries Factory is considered as a case study. Moreover, a sensitivity analysis is conducted for validating the achieved results. Findings indicate that the proposed model is a beneficial and applicable tool for risk assessment.

To the best of authors’ knowledge, no research has considered the weights of failure modes, the weights of risk factors and the priority weights of decision-makers simultaneously in the FMEA method.

Most decision-making problems in construction are complex and difficult to solve, as they involve multiple criteria and multiple decision makers in addition to subjective uncertainties, imprecisions and vagueness surrounding the decision-making process. In many instances, the decision-making process is based on linguistic terms rather than numerical values. Hence, structured fuzzy consensus-reaching processes and fuzzy aggregation methods are instrumental in multi-criteria group decision-making (MCGDM) problems for capturing the point of view of a group of experts. This chapter outlines different fuzzy consensus-reaching processes and fuzzy aggregation methods. It presents the background of the basic theory and formulation of these processes and methods, as well as numerical examples that illustrate their theory and formulation. Application areas of fuzzy consensus reaching and fuzzy aggregation in the construction domain are identified, and an overview of previously developed frameworks for fuzzy consensus reaching and fuzzy aggregation is provided. Finally, areas for future work are presented that highlight emerging trends and the imminent needs of fuzzy consensus reaching and fuzzy aggregation in the construction domain.

With numerous and ambiguous sets of information and often conflicting requirements, construction management is a complex process involving much uncertainty. Decision makers may be challenged with satisfying multiple criteria using vague information. Fuzzy multi-criteria decision-making (FMCDM) provides an innovative approach for addressing complex problems featuring diverse decision makers’ interests, conflicting objectives and numerous but uncertain bits of information. FMCDM has therefore been widely applied in construction management. With the increase in information complexity, extensions of fuzzy set (FS) theory have been generated and adopted to improve its capacity to address this complexity. Examples include hesitant FSs (HFSs), intuitionistic FSs (IFSs) and type-2 FSs (T2FSs). This chapter introduces commonly used FMCDM methods, examines their applications in construction management and discusses trends in future research and application. The chapter first introduces the MCDM process as well as FS theory and its three main extensions, namely, HFSs, IFSs and T2FSs. The chapter then explores the linkage between FS theory and its extensions and MCDM approaches. In total, 17 FMCDM methods are reviewed and two FMCDM methods (i.e. T2FS-TOPSIS and T2FS-PROMETHEE) are further improved based on the literature. These 19 FMCDM methods with their corresponding applications in construction management are discussed in a systematic manner. This review and development of FS theory and its extensions should help both researchers and practitioners better understand and handle information uncertainty in complex decision problems.

Multi-criteria methods represent an adequate tool for solving complex decision problems that provide real support to the decision maker in the choice process. This paper analyzes a decision problem that recurs over time using one of the newer methods as the Parsimonious AHP.

In this paper we integrated the P-AHP with: (1) the weighted average which takes into account the objectivity of the data; (2) ordered weighted average (OWA) aggregation operators that address the subjective nature of the data; (3) the Choquet integral and (4) the Sugeno integral which also considers the uncertain nature of the final ranking as it is defined on a fuzzy measure.

The present paper proves that variations in the final ranking, due to the different mathematical properties of the selected aggregators, are fundamental to select the best alternative without neglecting any characteristic of the input data. In fact, it is discussed and underlined how and why the best alternative is one that never excels but has very good positions with respect to all aggregation operator rankings.

The aim and innovation presented in this work is the use of the Parsimonious AHP (P-AHP) method in a dynamic way with the use of different aggregation techniques.

The purpose of this paper is to present a new public bread factory location selection for Istanbul Metropolitan Municipality (IMM).

A two-stage methodology is proposed to determine the location for the public bread factory facility. This framework is based on both geographic information systems (GIS) and multi-criteria decision-making (MCDM) techniques. The first stage of the methodology aims to decrease the number of possible alternative locations to simplify the selection activity by applying GIS; the second stage utilises interval type-2 fuzzy MCDM approach to exactly determine the public bread factory site location.

In this study, the authors present weighted normalised-based interval type-2 hesitant fuzzy and interval type-2 hesitant fuzzy sets (IT2HFSs)-based compressed proportional assessment (COPRAS) methods to overcome facility location selection problem for a fourth public bread factory in Istanbul.

The results show that the proposed approach is practical and can be employed by the bakery industry.

In this study, the authors present a two-stage methodology for public bread factory site selection. In the first stage, the number of alternatives is reduced by the GIS. In the second stage, an interval type-2 fuzzy set is implemented for the evaluation of public bakery factory site alternatives. A new integrated approach based on COPRAS method and weighted normalised with IT2HFSs is proposed.

The problem of evaluating potential suppliers has always been based on finding an optimal tradeoff between supplier’s performance consistently meeting firms’ needs and acceptable cost. The purpose of this paper is to propose a hybrid multi-criteria decision framework to quantify this qualitative judgment and reduce ambiguity in selection of suppliers in the era of Industry 4.0.

A hybrid intuitionistic fuzzy entropy weight-based multi-criteria decision model with TOPSIS is proposed. The authors make use of the intuitionistic fuzzy weighted approach operator for aggregating individual decision maker’s opinions regarding each alternative over every criterion. Additionally, the authors employ the concept of Shannon’s entropy to calculate the criteria weights.

Results obtained on the basis of the proposed hybrid methodology are analyzed against two more cases wherein the authors try to showcase the relevance of using IFS and entropy-based decision framework and find out the uniqueness of the proposed framework in supplier selection process.

The proposed model is apposite to solve management problem of supplier selection in two ways: aggregating individual decision maker’s opinion for each of the predefined criteria along with individual decision maker’s importance and ranking the suppliers based on both positive and negative ideal solutions using TOPSIS.

A robust framework incorporates not only suppliers’ performance but also provides weightage to key decision makers. Especially in the context of MCDMs wherein both qualitative and quantitative data is evaluated simultaneously, the proposed framework is unique in its practical implementation of reducing ambiguity in the supplier selection process.

This study aims to improve the reliability of emergency safety barriers by using the subjective safety analysis based on evidential reasoning theory in order to develop on a framework for optimizing the reliability of emergency safety barriers.

The emergency event tree analysis is combined with an interval type-2 fuzzy-set and analytic hierarchy process (AHP) method. In order to the quantitative data is not available, this study based on interval type2 fuzzy set theory, trapezoidal fuzzy numbers describe the expert's imprecise uncertainty about the fuzzy failure probability of emergency safety barriers related to the liquefied petroleum gas storage prevent. Fuzzy fault tree analysis and fuzzy ordered weighted average aggregation are used to address uncertainties in emergency safety barrier reliability assessment. In addition, a critical analysis and some corrective actions are suggested to identify weak points in emergency safety barriers. Therefore, a framework decisions are proposed to optimize and improve safety barrier reliability. Decision-making in this framework uses evidential reasoning theory to identify corrective actions that can optimize reliability based on subjective safety analysis.

A real case study of a liquefied petroleum gas storage in Algeria is presented to demonstrate the effectiveness of the proposed methodology. The results show that the proposed methodology provides the possibility to evaluate the values of the fuzzy failure probability of emergency safety barriers. In addition, the fuzzy failure probabilities using the fuzzy type-2 AHP method are the most reliable and accurate. As a result, the improved fault tree analysis can estimate uncertain expert opinion weights, identify and evaluate failure probability values for critical basic event. Therefore, suggestions for corrective measures to reduce the failure probability of the fire-fighting system are provided. The obtained results show that of the ten proposed corrective actions, the corrective action “use of periodic maintenance tests” prioritizes reliability, optimization and improvement of safety procedures.

This study helps to determine the safest and most reliable corrective measures to improve the reliability of safety barriers. In addition, it also helps to protect people inside and outside the company from all kinds of major industrial accidents. Among the limitations of this study is that the cost of corrective actions is not taken into account.

Our contribution is to propose an integrated approach that uses interval type-2 fuzzy sets and AHP method and emergency event tree analysis to handle uncertainty in the failure probability assessment of emergency safety barriers. In addition, the integration of fault tree analysis and fuzzy ordered averaging aggregation helps to improve the reliability of the fire-fighting system and optimize the corrective actions that can improve the safety practices in liquefied petroleum gas storage tanks.

The purpose of this paper is to investigate a fuzzy hybrid approach for ranking the flare gas recovery methods and allocating to refineries.

The proposed approach is containing four stages: in the first stage, experts' assessment is applied to identify relevant criteria and sub-criteria in the evaluation of flare gas recovery methods. In the second stage, the corresponding weights of criteria and sub-criteria are determined via fuzzy decision-making trial and evaluation (DEMATEL)-analytical network process (ANP) (DANP) method. In the third stage, the flare gas recovery methods are ranked using fuzzy weighted aggregated sum product assessment method (WASPAS) multi-criteria decision-making (MADM) technique. In the fourth stage, an optimization model is developed to allocate gas recovery methods to refineries while maximizing the total utility of allocations based on model constraints.

According to the results of fuzzy DANP method, technical and operational criterion was the most important followed by economic, political, managerial and environmental criteria. With respect to sub-criteria, international sanctions and political stability were the most important. The results of fuzzy WASPAS method indicated that gas injection was the first ranked alternative. Finally, the mathematical modeling allocated the recovery methods to five refineries of South Pars gas field in Iran based on budget and time constraints.

The proposed approach provides a systematic tool in the selection of flare recovery methods and allocation to refineries. This approach uses a new combination of fuzzy DEMATEL-ANP (DANP) method, fuzzy WASPAS method and mathematical programming. The approach is effectively implemented in a case study for ranking the flare gas recovery methods and allocating to refineries of South Pars gas field in Iran.