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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.

Due to the increasing size and complexity of construction projects, construction engineering and management involves the coordination of many complex and dynamic processes and relies on the analysis of uncertain, imprecise and incomplete information, including subjective and linguistically expressed information. Various modelling and computing techniques have been used by construction researchers and applied to practical construction problems in order to overcome these challenges, including fuzzy hybrid techniques. Fuzzy hybrid techniques combine the human-like reasoning capabilities of fuzzy logic with the capabilities of other techniques, such as optimization, machine learning, multi-criteria decision-making (MCDM) and simulation, to capitalise on their strengths and overcome their limitations. Based on a review of construction literature, this chapter identifies the most common types of fuzzy hybrid techniques applied to construction problems and reviews selected papers in each category of fuzzy hybrid technique to illustrate their capabilities for addressing construction challenges. Finally, this chapter discusses areas for future development of fuzzy hybrid techniques that will increase their capabilities for solving construction-related problems. The contributions of this chapter are threefold: (1) the limitations of some standard techniques for solving construction problems are discussed, as are the ways that fuzzy methods have been hybridized with these techniques in order to address their limitations; (2) a review of existing applications of fuzzy hybrid techniques in construction is provided in order to illustrate the capabilities of these techniques for solving a variety of construction problems and (3) potential improvements in each category of fuzzy hybrid technique in construction are provided, as areas for future research.

Environmental decision making involving multiple stakeholders can benefit from the use of a formal process to structure stakeholder interactions, leading to more successful outcomes than traditional discursive decision processes. There are many tools available to handle complex decision making. Here we illustrate the use of a multicriteria decision analysis (MCDA) outranking tool (PROMETHEE) to facilitate decision making at the watershed scale, involving multiple stakeholders, multiple criteria, and multiple objectives. We compare various MCDA methods and their theoretical underpinnings, examining methods that most realistically model complex decision problems in ways that are understandable and transparent to stakeholders.

The United Nations Industrial Development Organisation (UNIDO) proposed the development of eco-industrial parks (EIP) related to the 9th, 12th, and 13th of the sustainable development goals (SDGs) for ensuring green industrial practice to deal with climate change. The first approach to EIP is choosing a suitable site, however, it is not a simple task, because it involves spatial factors and is always impaired by uncertainties that require more than one decision module. There is a fewer study in objectively assessing the criteria for the selection of suitable sites for EIP development to contribute to the SDG initiative. This study provides an integrated process for assessing a consistent weight of criteria for EIP site selection. Nine steps were used in the fuzzy-analytical hierarchy process namely criterion identification, hierarchical structure construction, triangular fuzzy number matrix, geometric ratio, fuzzy relative weight, defuzzification, normalisation, sensitivity analysis (SA) and weight ranking. When tested using spatial and non-spatial criteria for EIP site selection, results show transportation infrastructure (13%), raw materials (12%), water bodies (12%), climate (10%), labour (9%), land use (9%), markets (9%), governmental policies (8%), existing industries (7%), urban settlement (6%), and restricted areas (5%). The SA verified that any evaluation error of 2% or 5% on the criteria weight is insignificant, but for 10% error, results can be distorted. The study has developed a consistent, simple approach integrating hierarchical and uncertainty modules for choosing EIP locations, and it is proposed as a guide for selecting suitable greenfield or brownfield EIP sites for sustainable industrial practices.

It is known that model averaging estimators are useful when there is uncertainty governing which covariates should enter the model. We argue that in applied research there is also uncertainty as to which method one should deploy, prompting model averaging over user-defined choices. Specifically, we propose, and detail, a nonparametric regression estimator averaged over choice of kernel, bandwidth selection mechanism and local-polynomial order. Simulations and an empirical application are provided to highlight the potential benefits of the method.

New Technology Business Firms are known to be volatile dynamic organizations whose innovations are subject to short life cycles and product imitability. Venture capitalist firms who allocate funds to these start-ups need to evaluate multiple facets associated with the individual firm’s internal and external characteristics, as well as, its own unique objectives and goals. This study applies a multicriteria decision making model to the identification for venture capital firms of potential New Technology Business Firms who are requesting capital infusions.