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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 chapter investigates the potential of integrating multiple criteria decision-making (MCDM) techniques with decision support systems of digital supply chain management (DSCM) to achieve optimal outcomes. Digital supply chain (DSC) employs digital technologies (DTs) such as artificial intelligence (AI), Internet of Things (IoT), and big data analytics to provide extensive datasets and valuable insights pertaining to supply chain operations. MCDM techniques employ these realizations to facilitate informed decision-making through the assessment of multiple competing criteria. Usually MCDM approaches are used in the academic research with comparatively lesser application in industry. We argue that MCDM methodologies can play an instrumental role in DSCM, specifically in the areas of supplier selection, demand forecasting, and inventory management. Nevertheless, the integration of MCDM like AHP, ANP, DEMATEL, etc., with decision support systems presents several challenges, including concerns regarding the quality of data and the intricate task of assigning weights to various factors.

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.

Introduction: Decision making is always based on several factors which may affect the possible outcomes, especially in financial markets. Instead of having many criteria which may be required for decision making, “Multiple Criteria Decision Making” (MCDM) models might be used as a tool to reduce all criteria into a single one.

Purpose: The aim of this study is to measure the financial performance of commercial banks listed on Borsa Istanbul (BIST) by the MCDM.

Method: To this end, data from 15 different financial ratios from 11 commercial banks were used between the periods of 2002 and 2018. Both TOPSIS and gray relational analysis (GRA) models were used, which are commonly used in the literature for detecting the financial performance of listed banks in BIST based on their consolidated financial statements.

Results: According to the TOPSIS method, while the best bank is QNB Finansbank, HALKB, a public bank, was determined as the best bank using the GRA method. There is no significant correlation between financial performance indicators and market returns obtained by either method, with exceptions. There is no generally significant correlation detected between financial ratios and market returns. Accordingly, it is concluded that the bank stock prices in the study are shaped by the influence of external factors and expectations. The study results include information that can be used for different purposes among bank managers, academics and financial investors.

Pressure on business to direct their activities responsibly has been increased during the last years to extent their suitability performance in all economic, social and environmental dimensions. This has motivated businesses and researchers to identify ways to implement sustainable and resilient operations. In the era of economic globalisation, small and medium enterprises (SMEs) are recognised as an engine of sustainable economic development in both the developed and developing world. Their competitiveness drives the economy, both nationally and internationally. SMEs have faced challenges in developing, internationalisation and achieving competitive advantage. Purpose of current study is to identify and analyse the sustainability and resiliency (SR) barriers to SME internationalisation and prioritise the practices to overcome the negative influence of barriers. In this regard, first, barriers and innovative practices have been identified through the literature review. Second, the essential barriers will be selected through reduction steps by the intuitionistic fuzzy Delphi (IF-Delphi) method. After computing the weight of barriers through the IF-DEMATEL method, the practices were prioritised using four multiple attribute decision-making (MADM) methods in an IF environment. Finally, the scores were aggregated by correlation coefficient and standard deviation (CCSD) technique. Results present that ‘Lack of economical resources to global exports’ and ‘Complications in acclimatizing export product design’ are the top priority barriers and ‘Knowledge of global market opportunities’ and ‘Networking with business incubator institutions’ have been recognised as the essential SMEs internationalisation practices. This study contributes to creating a more focussed approach towards the growth of SMEs. The study results would be helpful for industry, policymakers and academia.

Many European businesses are small and medium enterprises (SMEs), contributing significantly to the well-being of local economies and regions. Even so, SMEs face many challenges in fostering innovation and improving performance. Furthermore, the raw material consumption is increasing globally, necessitating the development of strategies that will reduce the number of raw materials extracted and imported while improving the sustainability of small and medium-sized businesses. Consequently, promoting circular economy (CE) strategies, such as industrial symbiosis (IS) partnerships, whereby waste products from other industries serve as a source of raw materials for companies, is critical. Identifying and analysing enablers or drivers that support IS collaborations among SMEs is necessary to achieve this goal. In this regard, the purpose of this study will explore the enablers of IS among SMEs considering sustainability dimensions (environmental, social and economic). As facing a decision-making (DM) problem, the multiple attribute decision-making (MADM) approach was applied in a hesitant fuzzy (HF) environment in this research to answer the research questions. In this regard, in phase 1, IS enablers were identified and extracted using a literature review and experts’ opinions. In phase 2, the hesitant fuzzy Delphi (HFD) method was implemented to screen and finalise the enablers identified. In phase 3, casual relations among final enablers were determined using the hesitant fuzzy ANP (HF-ANP) method. Finally, in phase 4, the relative importance of enablers was calculated using the hesitant fuzzy best–worst method (HF-BWM). Consequently, this study provided potential strategies for IS that can be implemented quickly and used by local authorities to support SMEs in achieving circular waste management.

The study constructs a composite indicator to rank macroeconomic performance of countries and a separate composite indicator to rank countries by inequality using the TOPSIS methodology of Multiple Criteria Decision-Making Analysis. The intuitive idea of TOPSIS is to formulate an ideal solution with respect to each individual policy variable; the relative rank of any country is then determined, using a suitable distance metric, such that the best performer simultaneously has the shortest distance from the ideal solution and the farthest distance from the non-ideal. It uses the composite indicator based rankings together with the KOF Globalization Index and sub-indices based rankings to examine the overall relationship between globalization and macroeconomic performance of countries and reduction in inequality; the impacts of trade and financial globalization for 1990–2018 across countries and groups of the globe. It shows that though highly correlated with growth, globalization may not necessarily lead to an improvement in overall macroeconomic performances of countries when one also takes into account unemployment and inflation. Economic globalization is seen here to mostly coincide with rise in income inequality. Observations also support the fact that countries, even if they are not highly integrated may reap sufficient benefits of globalization for macroeconomic performance and inequality diminution given supportive policies.

A great deal of uncertainty accompanies predictions of the potential effects of global climate change on the coastal hazards associated with severe storms. One way to obviate the effects of this uncertainty on the design of policies is to understand the manner in which populations are currently vulnerable to these types of hazards. In this chapter, we develop a method for constructing a relative composite measure of vulnerability using data envelopment analysis (DEA). Through the application of this index, and one constructed using a weighted average, to four costal towns along Boston's North Shore, we demonstrate their potential usefulness to policy formulation and implementation. The DEA composite index is shown to complement the information provided by the weighted average and helps overcome some of its shortcomings such as assigning importance weights and masking of the influence of one or a subset of vulnerability attributes. Acknowledging the spatial implications of floodplain protection and mitigation efforts, the indices are constructed and analyzed at a number of different geographic scales.

Economic security is one of the crucial dimensions of the welfare state. High-income individuals are able to purchase private insurance, but a large portion of the individuals remains uninsured. The authors have tried to rationalize the problem of the study over the reason why people remain uninsured. Hence, the purpose of the study is to identify an insurance model that can cover the risk of the heterogeneous segments. The study is qualitative in nature and applies a fuzzy analytic hierarchy process (FAHP). Based on seven criteria, process is applied to arrive at an alternative model among basic models of insurance, namely, conventional private insurance, mutual, and social insurance. Since social insurance has emerged with the highest score of 41% in the study, it is implied that social insurance works best in a situation where the market is full of private information and moral hazard. The findings reaffirm that government intervention is required in an insurance market to provide coverage to both covariate and idiosyncratic risks. The findings are especially relevant in the context of emerging markets where a sizeable poor population goes uninsured. The study contributes to the literature by proposing alternative insurance to address the problem of insuring the voluntarily uninsured.