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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.

This study aims to determine the ranking of the 81 provinces at the NUTS-3 level in Turkey with respect to the personal satisfaction and public services satisfaction by applying Fuzzy Multi-Criteria Decision-Making methods to the Life Satisfaction Survey Results.

Fuzzy TOPSIS, Fuzzy MULTIMOORA and Fuzzy ARAS are implemented to assess life satisfaction of the individuals who lived in provinces, based on Life Satisfaction Survey 2013 for Turkey’s national comparison. In the solution process, 14 indicators for personal satisfaction and 38 indicators for public services satisfaction were considered.

The results showed that personal health satisfaction, earnings from work satisfaction and monthly income satisfaction are the most important criteria in terms of personal satisfaction. Also, healthcare services satisfaction, judicial services satisfaction and education services satisfaction have the highest importance in terms of public services satisfaction. The final ranking of the 81 provinces is obtained by considered methods. According to the ranking results, there is no significant difference between the east and the west part of Turkey in terms of personal satisfaction, whereas there is a distinct difference between them in terms of satisfaction with public services.

This study is the first research for evaluating the ranking of the provinces at the NUTS-3 level in Turkey according to the Life Satisfaction Survey 2013 results considering 14 indicators for personal satisfaction and 38 indicators for public services satisfaction by using FMCDM approaches that have not been applied before.

The objective of this paper is to present the employment of the new hierarchical fuzzy technique for order preference by similarity to ideal solution (TOPSIS) approach to evaluate the most suitable business process outsourcing (BPO) decision.

The paper explains the importance of selection criteria for evaluation of BPO. It then describes briefly the fuzzy hierarchical TOPSIS methodology. There then follows a discussion of the application of the fuzzy hierarchical TOPSIS with some sensitivity analysis to the BPO evaluation problem. Finally, some concluding remarks and perspectives are offered.

Use of the hierarchical fuzzy TOPSIS methodology offers a number of benefits. It is a more systematic method than the other fuzzy multi‐criteria decision‐making (FMCDM) methods and it is more capable of capturing a human's appraisal of ambiguity when complex multi‐criteria decision‐making problems are considered. The hierarchical fuzzy TOPSIS is superior to the other FMCDM methods, such as fuzzy analytic hierarchy process (FAHP) and classical fuzzy TOPSIS methods, since the hierarchical structure without making pairwise comparisons among criteria, sub‐criteria, and alternatives is considered. Hierarchical fuzzy TOPSIS is an excellent tool to handle qualitative assessments about BPO evaluation problems, and its calculations are faster than FAHP. Also, hierarchical fuzzy TOPSIS makes it possible to take into account the hierarchical structure in the evaluation model. However, there are drawbacks. The classical fuzzy TOPSIS is a highly complex methodology and requires more numerical calculations in assessing the ranking order of the alternatives than the hierarchical fuzzy TOPSIS methodology and hence it increases the effort, thus limiting its applicability to real world problems.

The proposed model will be very useful to managers in the manufacturing sector, as this method makes decision making easier, systematic, efficient and effective.

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.

This study aims to propose a framework for designing appropriate triangular fuzzy numbers in a fuzzy analytic hierarchy process (FAHP) for selecting a road freight transport route and investigate the appropriate fuzzy numbers determined from a reliable consensus among participating experts based on the Delphi method.

The fundamental nine-point scale was investigated and separated into nonfuzzy intervals for importance assessment. A total of 17 individual experts participated in this study, and their consensus was that appropriate fuzzy numbers could be obtained using a three-round Delphi process. Moreover, the appropriate fuzzy numbers were used instead of the primary fuzzy numbers to calculate the relative weights of the decision criteria for road freight route selection.

The results confirmed that the Delphi method can be easily and rigorously applied to define appropriate fuzzy numbers. Further, this framework can serve as a guideline for a situation wherein the input of other fuzzy multiple criteria decision-making tools must be provided.

The FAHP has been widely used to address the imprecise assessments of decision-makers. However, most existing studies on the incorporation of such techniques have not defined fuzzy numbers, which are relevant to the problem of interest. This study contributes by incorporating the Delphi method that can design an appropriate fuzzy number for road freight route selection.

Establishing a performance‐oriented evaluation in public sectors is the key to successful administrations. However, because of lacking relative comparable measuring standards, it is difficult to measure the relative performance of one unit while comparing to other units with regard to the multiple criteria decision making (MCDM) of performance evaluation. This paper aims to focus on the performance ranking of research and development (R&D) projects in Taiwan's public sectors.

The algorithm in this paper is based on the concept of fuzzy set theory and the hierarchical structure analysis. The analyzing method adopts the methods of standard normal distribution, linear transformation, and fuzzy MCDM, carrying on the analysis of multiple criteria of the performance evaluation.

This paper constructs linguistic values to the subjective judgments and analyzes the ranking results of the performance evaluation with respect to 45 R&D projects of one of Taiwan's electric power companies. Thus, the paper demonstrates a successful way of evaluating R&D projects in the public sector.

In this paper, a decision algorithm based on the fuzzy set theory is proposed to solve the performance evaluation of R&D projects in public sectors. In order to solve the difficulties of measuring one unit of the relative performance of quantitative criteria comparing to the other units, the method of standard normal distribution is adopted while measuring the quantitative criteria. The concept of linguistic values and fuzzy numbers are used in this paper since they could easily be used to describe the subjective measurement of the appropriateness of alternatives and the importance weightings of criteria.

Robot selection is basically a task of choosing appropriate robot among available alternatives with respect to some evaluation criteria. The task becomes much more complicated since apart from objective criteria a number of subjective criteria need to be evaluated simultaneously. Plenty of decision support systems have been well documented in existing literature which considers either objective or subjective data set; however, decision support module with simultaneous consideration of objective as well as subjective data has rarely been attempted before. The paper aims to discuss these issues.

Motivated by this, present work exhibits application potential of preference ranking organization method for enrichment evaluations (extended to operate under fuzzy environment) to solve decision-making problems which encounter both objective as well as subjective evaluation data.

An empirical case study has been demonstrated in the context of robot selection problem. Finally, a sensitivity analysis has been performed to make the robot selection process more robust. A trade-off between objective criteria measure and subjective criteria measure has been shown using sensitivity analysis.

Robot selection has long been viewed as an important decision-making scenario in the industrial context. Appropriate robot selection helps in enhancing value of the product and thereby, results in increased profitability for the manufacturing industries. The proposed decision support system considering simultaneous exploration of subjective as well as objective database is rarely attempted before.

To develop a multi‐attribute decision making model for evaluating and selecting among logistic information technologies.

First a multi‐attribute decision making model for logistic information technology evaluation and selection consisting of 4 main and 11 sub criteria is constructed, then a hierarchical fuzzy TOPSIS method is developed to solve the complex selection problem with vague and linguistic data. Sensitivity analysis is presented.

Reviews the literature and provides a structured hierarchical model for logistic information technology evaluation and selection based on the premise that the logistic information technology evaluation and selection problem can be viewed as a product of tangible benefits, intangible benefits, policy issues and resources. Defines tangible benefits as cost savings, increased revenue, and return on investment; intangible benefits as customer satisfaction, quality of information, multiple uses of information, and setting tone for future business; policy issues as risk and necessity level; resources as costs and completion time. Presents a methodology that is developed for the complex, uncertain and vague characteristics of the problem.

Comparisons with other multi‐attribute decision making techniques such as AHP, ELECTRE, PROMETHEE and ORESTE under fuzzy conditions can be done for further research.

This article is a very useful source of information both for logistic managers and stakeholders in making decisions about logistic information technology investments.

This paper addresses the logistic information technology evaluation and selection criteria for practitioners and proposes a new multi‐attribute decision making methodology, hierarchical fuzzy TOPSIS, for the problem.

The purpose of this paper is to introduce an integrated approach using failure modes and effects analysis (FMEA), multiple-criteria decision making (MCDM), mathematical modeling and quality function deployment (QFD) techniques, for risk assessment and service quality enhancement in coronary artery bypass grafting (CABG) as a treatment for cardiovascular diseases (CVDs).

First, the disruptions in the CABG process are identified and prioritized following FMEA instructions, using two MCDM techniques, called analytic hierarchy process (AHP) and TOPSIS. Consequently, several corrective activities are identified and weighted on the basis of QFD. Finally, a mathematical model is established to determine the most cost-effective activities for implementation. The approach is developed in a fuzzy environment to reflect the uncertainty and ambiguity of human reasoning.

Regarding the CABG process disruption, a total of 30 failure modes in four main categories were identified and prioritized. Moreover, eight corrective activities were devised and ranked according to their impact on the failure modes. Finally, considering a limited amount of budget, a sensitivity analysis on the mathematical model’s objective function indicated that using 30 percent of the total budget, required to implement all corrective activities, was enough to cover more than 70 percent of the effects of corrective activities on the failure modes.

This paper contributes to the quality risk assessment knowledge by introducing an integrated approach to evaluate and improve healthcare services quality. Also, the case study conducted on the CABG process has not been done by other related studies in the literature.