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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.

In the past decades, artificial intelligence (AI)-based hybrid methods have been increasingly applied in construction risk management practices. The purpose of this paper is to review and compile the current AI methods used for cost-risk assessment in the construction management domain in order to capture complexity and risk interdependencies under high uncertainty.

This paper makes a content analysis, based on a comprehensive literature review of articles published in high-quality journals from the years 2008 to 2018. Fuzzy hybrid methods, such as fuzzy-analytical network processing, fuzzy-artificial neural network and fuzzy-simulation, have been widely used and dominated in the literature due to their ability to measure the complexity and uncertainty of the system.

The findings of this review article suggest that due to the limitation of subjective risk data and complex computation, the applications of these AI methods are limited in order to address cost overrun issues under high uncertainty. It is suggested that a hybrid approach of fuzzy logic and extended form of Bayesian belief network (BBN) can be applied in cost-risk assessment to better capture complexity-risk interdependencies under uncertainty.

This study only focuses on the subjective risk assessment methods applied in construction management to overcome cost overrun problem. Therefore, future research can be extended to interpret the input data required to deal with uncertainties, rather than relying solely on subjective judgments in risk assessment analysis.

These results may assist in the management of cost overrun while addressing complexity and uncertainty to avoid chaos in a project. In addition, project managers, experts and practitioners should address the interrelationship between key complexity and risk factors in order to plan risk impact on project cost. The proposed hybrid method of fuzzy logic and BBN can better support the management implications in recent construction risk management practice.

This study addresses the applications of AI-based methods in complex construction projects. A proposed hybrid approach could better address the complexity-risk interdependencies which increase cost uncertainty in project.

The purpose of this paper is to present a detailed literature review of the recent applications of the analytic hierarchy process (AHP) and analytic network process (ANP) group decision‐making methodologies.

Among more than 600 related papers published in the period 2005‐2009, a total of 232 application articles published in highly reputed international academic journals were selected and referenced in this paper. Papers were categorized according to application areas, subject titles, publication date, country of origin, academic journals, and integrated methodologies, and are summarized herein by various tables and charts.

The findings show that during the years 2005‐2009, use of the AHP technique has continued to increase exponentially. Moreover, it is expected that ANP will gain more popularity in the future, as the benefits of ANP become better understood. Applications of AHP have been dominant in manufacturing, followed by the environmental management and agriculture field, power and energy industry, transportation industry, construction industry, and healthcare. Other remarkable application fields include education, logistics, e‐business, IT, R&D, telecommunication industry, finance and banking, urban management, defense industry and military, government, marketing, tourism and leisure, archaeology, auditing, and the mining industry.

The study does not consider theoretical based AHP or ANP articles. Also the search excluded conference proceedings, masters' theses, and doctoral dissertations.

It is hoped that this study will guide practitioners in future work towards advancement of these techniques and will help the managers to select better decisions by making use of these methodologies.

The paper presents a comprehensive literature review of recent applications of AHP, and also ANP decision tools over the period 2005‐2009. Furthermore, the paper covers fuzzy AHP and fuzzy ANP extensions that are becoming popular methods in some application areas of traditional AHP and ANP.

The purpose of this paper is to identify the barriers of knowledge management (KM) implementation in Alborz Province industrial Parks Corporation and prioritize them using fuzzy analytical network process (FANP).

Through an in-depth review of the literature on KM and researcher findings from observations and interviews with experts, the main barriers of KM implementation, namely, organizational culture, organizational structure, human resource, technology and miscellaneous factors along with their related factors in the surveyed organization were identified. Then, based on the information gathered, an expert questionnaire was developed. Finally, the priority of each main barrier and their sub-factors were determined using FANP.

The results show that human resource and organizational culture factors with the weights of 0.66 and 0.22, respectively, have the highest ranking and therefore are the most important barriers. The technology factor with the weight of 0.00002 is the least important barrier in implementing KM in the surveyed corporation.

One of the limitations of this study is the generalizability of the findings, which may be limited by the single case study method used.

There are fewer studies about KM barriers specifically with a focus on prioritizing them in organizations, especially in the context of Iran as a developing country. This study develops a comprehensive and solid mathematical technique to prioritize the identified barriers of KM implementation in the context of Iran.

Due to the important role of strategic human resources in fulfilling the main objectives of organizations on the one hand and the necessity of having suitable functional strategies in place to operationalize the developmental programs on the other hand, this study aims to identify the factors and sub-factors of developmental programs and their priorities as well as the relationship and interactions of the identified criteria in human capital developmental programs through a hybrid fuzzy decision-making trial and evaluation laboratory –analytic network process approach. Also, the rank of functional strategies to achieve these human resource developmental programs is determined using fuzzy VIsekriterijumska Optimizacija I KOmpromisno Resenje (VIKOR) technique.

Through an in-depth review of the relevant literature, the most important criteria and sub-criteria were determined. Then, a questionnaire was designed and distributed among 20 top managers and experts of the surveyed bank. Using geometric mean, the criteria were screened. In the next step, the second pairwise questionnaire was designed and distributed among eight experts, to determine the relations and interrelations among these factors their relevant sub-factors and prioritize them. Finally, using the third designed questionnaire and fuzzy, VIKOR (FVIKOR) technique the ranks of functional strategies were determined.

Analysis of the results showed that “future wellness and retirement” is the most influential factor and the “retention” factor is the most permeable factor. Also, human capital planning is the most important factor of this department’s developmental programs in achieving its strategic objectives. Factors “recruiting and hiring,” “retention,” “empowerment” and “future wellness and retirement” were ranked second to fifth, respectively. Finally, the application of the FVIKOR technique revealed that “enhancement and improvement of incentive systems” is the best functional strategy to achieve the developmental plans of the human capital department.

One of the limitations of this study is the generalizability of the findings, which may be limited by the single case study method used.

This study presents a comprehensive and effective tool which could specifically help policymakers and top managers of the survey company and other managers of the banking sector in general, to use a quantitative approach toward identification and prioritizations of the determinants factors of the human capital developmental programs toward achieving functional strategic objectives to enhance the satisfaction of their internal customer as the most important asset of their organizations which might lead to the increased external customer satisfaction and, subsequently, increased competitive advantage.

To the best knowledge of the authors, this is one the first studies of its kind which attempts through a hybrid fuzzy analytical network process and fuzzy DEMATEL approach, presents a structural network model to examine the interrelationships among the human capital developmental programs and prioritizes them, also simultaneously rank the functional strategies toward achieving these programs using FVIKOR technique.

The purpose of this paper is to evaluate the impact of lean criteria on leanness as well as prioritize them, taking the relationships between dimensions into consideration for manufacturing enterprises.

This study considers leanness over quality, cost, delivery and innovation (QCDI) performance dimensions. Twenty eight criteria related with these dimensions were determined that are focused on manufacturing organizations and then fuzzy analytic network process (ANP) approach was used to determine the influence value of each criterion on leanness.

The existing literature shows a lack of studies on systematically measuring the impact of lean criteria on leanness. To fill the gap, this paper presents a fuzzy ANP approach. Firstly interactions between the performance dimensions were configured. Then, according to the relationship, weights were obtained while taking the network structure that allows dynamic multidirectional relationships for interdependencies among performance dimensions into consideration. This provides a more accurate approach for determining the impact value on leanness performance in real-life decision-making environments.

The limitation of this study is that only manufacturing enterprises have been considered. Different criteria may need to be developed for service organizations.

This study gives a real insight to lean practitioners in the manufacturing system. Due to the fact it is difficult to achieve all the criteria at the same time for a company, this study is significant for manufacturers, indicating which criteria should primarily be focused on in order to achieve leanness.

Applying fuzzy ANP on interrelated QCDI performance dimensions to evaluate the impact of lean criteria on leanness is the novelty of this study in the related literature. The fuzzy ANP approach is thought to be a more suitable approach to obtain more realistic and accurate results with the power to cope with ambiguity. This study provides a systematic measurement of the influence of lean criteria, also considering interdependencies between performance dimensions. Another contribution of this study is adding innovation to the performance dimensions that are commonly known as quality, cost and delivery to assess leanness in a comprehensive manner.

Assessing a measure of sustainable supply chain management (SSCM) performance is currently a key challenge. The literature on SSCM is very limited and performance measures need to have a systematic framework. The recently developed balanced scorecard (BSC) is a measurement system that requires a balanced set of financial and non-financial measures. The purpose of this paper is to evaluate the SSCM performance based on four aspects i.e. sustainability, internal operations, learning and growth, and stakeholder.

This paper developed a BSC hierarchical network for SSCM in a close-loop hierarchical structure. A generalized quantitative evaluation model based on the Fuzzy Delphi Method (FDM) and Analytical Network Process (ANP) were then used to consider both the interdependence among measures and the fuzziness of subjective measures in SSCM.

The results of this study indicate that the top-ranking aspect to consider is that of stakeholders, and the top five criteria are green design, corporate sustainability, strategic planning for environmental management, supplier cost-saving initiatives and market share.

The main contributions of this study are twofold. First, this paper provides valuable support for supply chain stakeholders regarding the nature of network hierarchical relations with qualitative and quantitative scales. Second, this paper improves practical performance and enhances management effectiveness for SSCM.

The purpose of this paper is to develop a spatial intelligent decision support system (SIDSS) for increasing productivity in the rubber agroindustry by green productivity (GP) approach. The SIDSS was used to measure the productivity of rubber plantation and rubber agroindustry by GP approach, and select the best strategies for increasing the productivity of rubber agroindustry.

This system was developed by combining spatial analysis, GP, and fuzzy analytic network process (ANP) with the model-based management system, which is able to provide comprehensive and meaningful decision alternatives for the development of natural rubber agroindustry. Rubber plantation productivity measurement model was used to find the productivity level of rubber plantation with fuzzy logic, and also to provide information and decision alternatives to all stakeholders regarding spatial condition of rubber agroindustry, production process flow, and analysis of the seven green wastes at each production process flow using the geographic information system. GP measurement model was used to determine the productivity performance of the rubber agroindustry with the green productivity index (GPI). The best strategy for increasing the productivity was determined with fuzzy ANP.

Rubber plantation measurement model showed that the average of plantation productivity was 6.25 kg/ha/day. GP measurement model showed that the GPI value of ribbed smoked sheet (RSS) was 0.730, whereas of crumb rubber (CR) was 0.126. The best strategy for increasing the productivity of rubber agroindustry was raw material characteristics control. Based on the best strategy, the GPI value of RSS was 1.340, whereas of CR was 0.228.

This decision support system is still limited as it is based on static data; it needs further development so that it can be more dynamically based on developments in the rubber agroindustry related levels of productivity and environmental impact. In addition, details regarding the decision to increase the productivity of the rubber section by benchmarking efforts should be studied further, both among plantation as well as among countries such as Thailand so that the productivity of rubber plantation and agroindustry can be integrated.

This research can help the planters to select superior clones for rubber trees, to improve the technique of tapping latex, and to use a better coagulant. The good quality and quantity of raw material is a key factor in increasing the productivity of rubber agroindustry; if the quality of latex is good then the resulting product will also have a good quality and production cost can be reduced. In addition, the application of GP through the calculation of GPI value using improvement scenarios can be used as a reference and comparison for evaluating the performance of rubber agroindustry to reduce the waste generated by the activities of rubber processing plant.

Reduction of waste generated by production activities can improve the quality of life of the workforce and the environment. The calculation of GPI value can also be used as a basis to use raw materials, water, and electricity more efficiently.

This system was developed by combining spatial analysis, GP, and fuzzy ANP with the model-based management system, which is able to provide comprehensive and meaningful decision alternatives for the development of natural rubber agroindustry.

The paper aims to build a customized hybrid multi-criteria model to identify the top three utilities of drones at both personal and community levels for two use cases: firefighting in high-rise buildings and logistic support.

A hybrid multi-criterion model that integrates fuzzy analytical hierarchy process (AHP), Best Worst, fuzzy analytical network process (ANP), fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) is used to compute the criteria weights. The weights are validated by a novel ensemble ranking technique further whetted by experts at the community and personal levels to two use cases.

Drones' fire handling and disaster recovery utilities are the most important to fight fire in high-rise buildings at both personal and community levels. Similarly, drones' urban planning, municipal works and infrastructure inspection utilities are the most important for providing logistics support at personal and community levels.

The paper presents a novel multi-criteria approach, i.e. ensemble ranking, by combining the criteria ranking of individual methods – fuzzy AHP, Best-Worst, fuzzy ANP and fuzzy DEMATEL – in the ratio of optimal weights to each technique to generate the consolidated ranking. Domain experts also validate this ranking for robustness. This paper demonstrates a viable methodology to quantify the utilities of drones and their capabilities. The proposed model can be recalibrated for different use case scenarios of drones.

The purpose of this paper is to apply a fuzzy LinPreRa cognitive map to evaluate the interaction and importance of factors affecting the development of strategic alliance partnerships between the outlying island duty-free shops and existing collaboration firms in duty-free shops. Meanwhile, the key factors should be considered in establishing strategic alliance partnerships while analyzing and comparing the perspectives of owners for outlying island duty-free shop and partner vendors of collaborating firms along with differences of influencing key factors on partnership quality.

This study incorporates a fuzzy linguistic preference relation analytical network process (fuzzy LinPreRa ANP) in the fuzzy cognitive map (FCM) method to formulate a fuzzy LinPreRa cognitive map to evaluate the interactions and importance of key factors and the conditions of interactive impacts during the establishment of strategic alliance partnerships. The authors use the outlying island duty-free shops in Taiwan as the empirical subject to illustrate how the fuzzy LinPreRa cognitive map is applied. In-depth, interviews and questionnaire surveys are conducted to collect and evaluate respondents concerning key factors affecting strategic alliance partnerships establishment.

The following three findings based on the results of empirical analysis: first, the administrative behavioral patterns of managers for strategic alliance partnerships encompass shared values and goal coherence, while the associative statements are located on the first layer of fuzzy LinPreRa cognitive map core associations, which illustrates that businesses attach great importance to conceptual ideas. Second, integrity and reputations of both parties are the governing mechanism of strategic alliance partnerships, influencing mutual reputation. Third, the relationship of strategic alliance partnerships refers to the profit opportunities of both parties and their ability to respond to the market, including future development, regional indicators, marketing capabilities, brand multiplicity and customer retention. However, it can be inferred that such associative factors are located in the outer layer or belong to noncore associations, which means that both parties’ abilities to respond to market reactions are weakened.

This study provides valuable relationship managerial strategies to maintain long-term partnerships for outlying island duty-free shops and their alliance collaborating firms including strengthened relationships of both parties’ managers to achieve common values and consistent objectives; improved beneficial value of both parties in strategic alliance partnerships; continued close communications to enhance the quality of strategic alliance partnerships; and establishment of personnel training mechanisms and strict formulation of management rules for strategic alliance partnerships.

The main valuable contributions are included the fuzzy LinPreRa cognitive map by combining two different decision methods including FCM and fuzzy LinPreRa ANP is proposed to help decision makers to improve the evaluation quality and calculation efficiency for critical elements’ interaction and importance; the fuzzy LinPreRa cognitive map can clarify considering significant factors when maintaining strategic alliance partnerships and further provide valuable relationship managerial strategies to maintain long-term relationships for duty-free shop owners and their alliance collaborating firms.