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Historically, public and private sector enterprises have been viewed as existing on opposing ends of the performance measurement spectrum, due to seemingly incompatible worldviews. Private sector enterprises are traditionally viewed as profit-driven and focused on a return on investment paradigm, while public sector enterprises are seen as mission-oriented and answerable to a paradigm less focused on investment and more on improving enterprise capability. The authors propose that, in fact, these worldviews are not mutually exclusive, as private and public sector enterprises must both account for investment and mission concerns. In order to leverage real synergy to be gained from distinct but complementary viewpoints, a systemic approach to evaluating organization performance through the novel fusion of operational test and evaluation and multi-criteria decision analysis is developed. Use of this framework is demonstrated within an enterprise that involves consideration of public and private sector concerns. The authors hope that the approach proposed in this chapter will enable public and private sector enterprises to comprehensively address performance.

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.

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.

This section describes sustainable development (SD) in relation to infrastructure projects and explains how to evaluate SD. SD is assessed as context-dependent, considering the project’s economic, social, and ecological context. Sustainable road infrastructure projects (SRIP) should encapsulate the complete life cycle from idea to development, functionality, and maintenance. SD should be considered as part of the evaluation process prior to project execution, but it can also serve other functions. Sustainability evaluation must start with project appraisal or evaluation and the earliest stages of project decision-making. Sustainable infrastructure projects (SIPs) are evaluated using a variety of techniques and models, such as cost-benefit analysis (CBA), multi-criteria techniques, ecological and societal impact assessments, ranking techniques, models, and evaluation guidelines. Established SD structures and modelling techniques for infrastructure projects are presented from an SD perspective, with the primary objective of investigating how they operate and determining whether existing models provide an effective method for applying the SD idea into infrastructure development. CBA is a widely used strategy for evaluating alternatives to maximize sociocultural well-being. It is based on the likelihood of costing customer advantages and negative impacts and has been discussed in scholarly articles. The multi-criteria decision analysis (MCDA) approach is an acceptable methodology for addressing complex matters involving high risk, conflicting objectives, different types of information and data, different concerns and points of view, and the representation of complex and evolving biological, ecological, and financial frameworks. It combines many methodologies and offers various advantages over more conventional ways of decision-making and plan development. It should be used to increase community participation and empower partner organizations and should apply several criteria at the same time, including those that are difficult to adjust and quantify. The key difficulty with this strategy is the usage of weightings, which has been sharply criticized by several researchers. Life-cycle assessment (LCA) is an adaptive tool used to assess the ecological effects of a particular action, task, or procedure. It is applied globally to decision-making in numerous fields, including transportation, energy, and water, and has become a typical tool for determining the ecological performance of infrastructure projects. However, it has a few flaws and could benefit from improvements to assess SD with greater precision. It is a fragmented mechanism for assessing the three components of SD, but its incorporation into other evaluation approaches is desirable. The evaluation of societal implications has been conducted using a variety of methods and techniques, but there is currently no standard method for assessing the communal and appropriation consequences of infrastructure initiatives. Social life-cycle assessments (SLCAs) are advancing, but consensus remains a challenge. The Evaluation Partnership and the Centre for European Policy Studies identified several obstacles and challenges to implementing an outstanding societal assessment, such as the term ‘societal impacts’ being potentially overbroad and not adequately defined, and the lack of a suitable method for quantitatively evaluating sociological effects. Additionally, a large section of societal assessments is biased and frequently inconsequential. The chapter discussed the theoretical and methodological stances on sustainable road infrastructure, using current SID concepts and evaluation techniques thoroughly.

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.

In this chapter, the integrative methodological approach (IMA) of the research project GLOWA Elbe is introduced, which represents a scientific methodology to support water management under uncertainty regarding future paths of global change. The approach paves the way for integration of research work of many disciplines, of different assessment methods, of various policy fields, and the involvement of relevant stakeholders and decision makers. IMA can be roughly described by four research elements (scenario derivation, indicator and criteria identification, model-based impact analysis, and final scenario assessment based on combined benefit–cost and multi-criteria analysis), which lay the basis for the IMA activities of the global change research sequence. Its practical application is demonstrated by a case study on the Spree and Schwarze Elster river basins. Specific results of Chapter 4 (on scenario derivation) and Chapter 11 (on integrating economic evaluation into water management simulation) in this volume are picked up in order to focus on the illustration of the integrated assessment results for this German case study.

When comparing and evaluating performance, decision-makers are concerned with providing a range of effective, efficient, and fair measures that can yield representative relative rankings for the units being evaluated. In this chapter, we apply three multicriteria benchmarking modeling techniques – weighted linear combination, data envelopment analysis (DEA), and ordered weighted average (OWA) – to an example dataset to provide a quantitative assessment of performance. Evaluation of the results demonstrates that each of these techniques has relative strengths and shortcomings. To take advantage of the relative strengths, and avoid some of the shortcomings that we observed, we develop and assess a promising new methodological approach, the order rated effectiveness (ORE) model. ORE uses the OWA unit ratings within a DEA optimization framework to provide an overall relative performance assessment.

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.

The chapter examines a comprehensive review of cross-disciplinary literature in the domain of supply chain forecasting during research period 1991–2017, with the primary aim of exploring the growth of literature from operational to demand centric forecasting and decision making in service supply chain systems. A noted list of 15,000 articles from journals and search results are used from academic databases (viz. Science Direct, Web of Sciences). Out of various content analysis techniques (Seuring & Gold, 2012), latent sementic analysis (LSA) is used as a content analysis tool (Wei, Yang, & Lin, 2008; Kundu et al., 2015). The reason for adoption of LSA over existing bibliometric techniques is to use the combination of text analysis and mining method to formulate latent factors. LSA creates the scientific grounding to understand the trends. Using LSA, Understanding future research trends will assist researchers in the area of service supply chain forecasting. The study will be beneficial for practitioners of the strategic and operational aspects of service supply chain decision making. The chapter incorporates four sections. The first section describes the introduction to service supply chain management and research development in this domain. The second section describes usage of LSA for current study. The third section describes the finding and results. The fourth and final sections conclude the chapter with a brief discussion on research findings, its limitations, and the implications for future research. The outcomes of analysis presented in this chapter also provide opportunities for researchers/professionals to position their future service supply chain research and/or implementation strategies.