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The purpose of this paper is to examine the possibility of applying non‐structural fuzzy decision support theory in modelling the negotiation between various participants with conflicting objectives so as to establish the optimum buffer stocks for a construction project.

In view of the divergence in objectives between various decision makers when determining the amount of materials to be delivered to site, the concept of integrating the non‐structural fuzzy decision support system (NFDSS) to multi‐attribute decision making is reviewed. With the help of a case study example, the process involved in the NFDSS and the methodology of evaluation are illustrated. Finally, the paper proposes the use of the Nash criterion to measure the utility of various decision makers so as to identify an equilibrium solution on the quantity of materials to be supplied.

The results indicate that the requisite number of on‐site stocks can be determined by referring to the utility of the parties involved in decision making.

The NFDSS systematically evaluates each scenario under different affected factors such as cost, schedule, quality, safety, etc. Finally, a scenario utility is computed to establish the preference of each party.

The paper is of value in showing how NFDSS can systematically analyse human judgements to generate relative weightings for the decision factors and elements. The NFDSS model can be applied to real‐world cases to determine the frequency of delivery and the amount of buffer stocks that would meet the interest of various project participants.

The purpose of this paper is to unveil how Johor Bahru resident’s conceptions are affected by various structural housing attributes. The determination of an appropriate combination of housing preferences requires a great deal of involvement to balance a complex interplay of factors. This is attributable to this fact that housing preferences involve multiple-criteria decision-making, as each element has its own perceived relative importance. Accordingly, this study examines the resident housing preferences of Johor Bahru located in southern Malaysia with specific emphasis on the priority of structural attributes of housing preferences.

Using data collected through the theoretical and conceptual framework of non-structural fuzzy decision support system (NSFDSS) applied to facilitate the priority setting process.

Priority lists of preferences for structural housing attributes showing the contribution of each factor within their respective level and not the contribution of that factor toward the overall housing preferences were established.

By providing primary information about how potential buyers of a new house prioritize the different elements of a house design, the obtained results can make a useful contribution to the knowledge of individuals engaged in the housing development industry.

While in examining consumers’ housing choice and preferences, most studies use the hedonic price framework studying the issue from several aspects, this is first study dedicated to establish a priority of housing attribute using NSFDSS.

To develop a multi‐criteria decision‐making framework for evaluating different schemes of urban regeneration project.

Urban decay in Hong Kong has long warranted community concern. To tackle the problem, there are generally two different approaches, namely building rehabilitation and redevelopment. In the past, urban regeneration was dominated by complete redevelopment. However, with the rise of the concept of sustainability, the choice of building rehabilitation is becoming increasingly popular. Nevertheless, with either option, difficulties are often encountered in balancing the diverse interests of the stakeholders, who have varied aims and ambitions for the achievements of a project. Therefore, we have developed a framework, which contains the factors to be considered when planning an urban renewal project. To obtain the relative importance of these factors in a reliable but reasonably inexpensive manner from the building‐related professionals, the Non‐structural Fuzzy Decision Support System was employed. In total, 34 building surveyors and 31 town planners were interviewed using structured questionnaires.

The set of perceived weightings of the decision criteria obtained from building surveyors was quite different from that from town planners. People of different backgrounds hold divergent views towards the relative importance of the decision criteria in an urban regeneration project.

Due to the resource limitations, individuals from the professions of building surveying and town planning were studied only.

To achieve a credible decision‐making process, it is therefore advisable to have a balanced mix of members in any urban regeneration project decision making panel. Also, the framework developed in this study can be used to facilitate the decision making process in the future project.

This paper is the first attempt to explore the relative importance of various criteria for the decision‐making process in urban regeneration projects perceived by different professionals.

Resilient hospitals have the vital role in reducing mortality, severity of injuries by providing required emergency services during accidents and disasters. This study aims to identify and prioritize key indicators on hospital resilience.

This cross-sectional study was conducted in 2019. The draft of the indicators obtained from the systematic review of the previous study was finalized, with three expert panel sessions and 14 experts in resilience fields. The outputs of these sessions were divided into three domains including constructive resilience, infrastructural resilience and administrative resilience, 17 sub-domains and 71 indicators. Then fuzzy analytic network process method was used to weight and prioritize the final indicators of hospital disaster resilience.

Administrative resilience, logistic and financial management and strategic outsourcing agreement allocated the highest weight as domain, sub-domains and indicators, respectively. The weight of each sub-domain and indicator was also determined.

Investigating the weight of domains, sub-domains and indicators shows the importance of managerial and operational issues in hospital resilience. By using the indicators and relative weights, a tool for measuring hospital disaster resilience can be created in further studies. The output of these assessments is effective in promoting safety and increasing awareness of hospital managers and health policymakers.

The regular occurrence of natural disasters elevates the need for an effective method to measure organizational preparedness in responding to the adverse impact of disasters. In this context, this paper presents a new decision support model to assess organizational disaster preparedness using both subjective and objective disaster preparedness criteria in a multi-criteria decision-making context.

The statistical variance method is integrated with the proximity value index (PVI) technique to determine priority scores in order to rank organizational disaster readiness.

The results of applying the integrated model developed herein enable decision-makers to make informed decisions for assigning priority ranking of organizational disaster preparedness in a simpler and more efficient way.

Human resource is the most impacting criterion affecting hospital preparedness in undertaking action to cure disaster victims.

This paper offers an exemplar of a simple and efficient decision-making process considering the subjectivity associated with decision-making as well as the objectivity of data used for determining the priority ranking of organizational disaster preparedness.

Integrating statistical variance method with the PVI technique is novel and it has not been presented in previous studies. In fact, this study is the first to integrate both methods for selecting the priority ranking of organizational disaster preparedness.

Advanced manufacturing machines (AMMs) are searched as a momentous asset across the manufacturing societies for quenching and addressing the production units under economical circumstances, i.e. production of high-quality of goods under feasible cost. AMMs are significant in holding the managers against their rivals and competitors with high profit margins. The authors developed the decision support mechanism/portfolio (DSM-P) consist of knowledge-based cluster approach with a dynamic model. The purpose of research work is to measure overall economic worth of AMMs under objective and grey-imperfect (mixed) data by exploring the proposed DSM-P.

The authors developed the DSM-P that consist of knowledge-based cluster, three multi-criteria decision-making (MCDM) techniques-1-2-3 with complementary grey relational analysis-4(GRA), approach with a dynamic model (complied by technical plus cost and agility measures of AMMs). The proposed DSM-P enables the manager to map the overall economic worth of candidate AMMs under objective and grey-mixed data.

The presented DSM-P assist the managers for handling the selection problem of AMMs, i.e. CNCs, robots, automatic-guided vehicle, etc under mixed (objective cum grey) data. To enable the readers for intensely understand the work, the utility of proposed approach is displayed by illustrating a polar robot evaluation and selection problem. It is ascertained that the robot candidate-11 alternative is fulfilling the entire technical cum cost and agility measures.

The DSM-P provides more precise and reliable outcomes due to a usage of the dominance theory. Under the dominance theory, the ranks are obtained by MCDM techniques-1-2-3 are compared with ranks gathered by the GRA-4 under objective cum grey data, formed the novelties in presented research work. From a future perspective, the grey-based models in DSM-P can be built/extended/constructed more extensive and can be simulated by the same approach.

Concrete structures undergo early and fast deterioration, which causes defects such as cracks, water leaks and delamination, resulting from a lack of or inefficient maintenance practices. To improve this behaviour, this paper aims to develop a maintenance strategy benchmarking model for concrete structures.

Fuzzy logic toolbox on MATLAB R2018a was used to develop the proposed model and it was applied to two cases. A comprehensive literature search was done to review common concrete defects, their impact on the performance and functionality of the structure, effectiveness of maintenance strategies and previous maintenance benchmarking models. The literature findings were further validated through expert interviews which have been incorporated in the model.

Case study results show that preventive maintenance (PM), predictive maintenance (PdM) and corrective maintenance (CM) strategies are required more or less in similar combinations for maintenance of concrete roof structures. The best combination for case 1 is 36.42% PM, 35.40% PdM and 28.18% CM, and for case 2 is 35.93% PM, 35.08% PdM and 28.99% CM. According to suitability, they can be ranked as PM > PdM > CM.

This model will contribute as a comprehensive decision-making tool for building/facility managers. The findings further carry a strong message to those who practice only CM in their buildings.

Construction and demolition waste (CDW) continuously causes environmental and social problems. These formidable challenges lead to sustainable issues and are an increasingly urgent issue worldwide. Prior studies have neglected to link the triple bottom line (TBL) to a reliable estimation or empirical model for estimating CDW production performance and lack empirical sensitivity analysis in profit maximization. This study proposes an attribute analysis to build a cost–benefit analysis (CBA) to obtain profit maximization.

This study uses fuzzy set theory to develop a cost and benefit analysis (CBA) model to assess the sensitivity analysis in terms of its performance on addressing the environmental, economic and social aspects. The model is used to weigh the sum of benefits such as financial gain and total costs of actions taken to mitigate the negative impacts.

Based on the sensitivity analysis conducted, the environmental, economic and social mean scales were significantly changed, i.e. increased, and profits increased drastically. The results provide an insight into environmental legislation compliance, environmental investment and environmental impact as the cause attributes for the CDW recycling profit increase. The results prove that sensitivity analysis is viable to infer that a sustainable production performance can achieve more revenue and profit through an adequate selection of attributes regarding the TBL aspects to address the firm's uncertainty problem in multiple criteria analysis.

This study builds a CBA model to maximize profits for recycled CDW material by linking of environmental, economic and societal aspects for recycled CDW assessments. It considers a sustainability structure with criteria based on TBL aspects to assess production performance to realize the Sustainable Development Goals and presents fuzzy set theory and sensitivity analysis to solve the uncertainty problem in the construction industry.

The purpose of this paper was to develop an integrated framework for assessing the flood risk and climate adaptation capacity of an urban area and its critical infrastructures to help address flood risk management issues and identify climate adaptation strategies.

Using the January 2011 flood in the core suburbs of Brisbane City, Queensland, Australia, various spatial analytical tools (i.e. digital elevation modeling and urban morphological characterization with 3D analysis, spatial analysis with fuzzy logic, proximity analysis, line statistics, quadrat analysis, collect events analysis, spatial autocorrelation techniques with global Moran’s I and local Moran’s I, inverse distance weight method, and hot spot analysis) were implemented to transform and standardize hazard, vulnerability, and exposure indicating variables. The issue on the sufficiency of indicating variables was addressed using the topological cluster analysis of a two-dimension self-organizing neural network (SONN) structured with 100 neurons and trained by 200 epochs. Furthermore, the suitability of flood risk modeling was addressed by aggregating the indicating variables with weighted overlay and modified fuzzy gamma overlay operations using the Bayesian joint conditional probability weights. Variable weights were assigned to address the limitations of normative (equal weights) and deductive (expert judgment) approaches. Applying geographic information system (GIS) and appropriate equations, the flood risk and climate adaptation capacity indices of the study area were calculated and corresponding maps were generated.

The analyses showed that on the average, 36 (approximately 813 ha) and 14 per cent (approximately 316 ha) of the study area were exposed to very high flood risk and low adaptation capacity, respectively. In total, 93 per cent of the study area revealed negative adaptation capacity metrics (i.e. minimum of −23 to <0), which implies that the socio-economic resources in the area are not enough to increase climate resilience of the urban community (i.e. Brisbane City) and its critical infrastructures.

While the framework in this study was obtained through a robust approach, the following are the research limitations and recommended for further examination: analyzing and incorporating the impacts of economic growth; population growth; technological advancement; climate and environmental disturbances; and climate change; and applying the framework in assessing the risks to natural environments such as in agricultural areas, forest protection and production areas, biodiversity conservation areas, natural heritage sites, watersheds or river basins, parks and recreation areas, coastal regions, etc.

This study provides a tool for high level analyses and identifies adaptation strategies to enable urban communities and critical infrastructure industries to better prepare and mitigate future flood events. The disaster risk reduction measures and climate adaptation strategies to increase urban community and critical infrastructure resilience were identified in this study. These include mitigation on areas of low flood risk or very high climate adaptation capacity; mitigation to preparedness on areas of moderate flood risk and high climate adaptation capacity; mitigation to response on areas of high flood risk and moderate climate adaptation capacity; and mitigation to recovery on areas of very high flood risk and low climate adaptation capacity. The implications of integrating disaster risk reduction and climate adaptation strategies were further examined.

The newly developed spatially explicit analytical technique, identified in this study as the Flood Risk-Adaptation Capacity Index-Adaptation Strategies (FRACIAS) Linkage/Integrated Model, allows the integration of flood risk and climate adaptation assessments which had been treated separately in the past. By applying the FRACIAS linkage/integrated model in the context of flood risk and climate adaptation capacity assessments, the authors established a framework for enhancing measures and adaptation strategies to increase urban community and critical infrastructure resilience to flood risk and climate-related events.

This paper illuminates the distinction between individual and organizational actors in business-to-business markets as well as the coexistence of formal and informal mechanisms of coordination in multinational corporations. The main questions addressed include the following. (1) What factors influence the occurrence of personal contacts of foreign subsidiary managers in industrial multinational corporations? (2) How such personal contacts enable coordination in industrial markets and within multinational firms? The theoretical context of the paper is based on: (1) the interaction approach to industrial markets, (2) the network approach to industrial markets, and (3) the process approach to multinational management. The unit of analysis is the foreign subsidiary manager as the focal actor of a contact network. The paper is empirically focused on Portuguese sales subsidiaries of Finnish multinational corporations, which are managed by either a parent country national (Finnish), a host country national (Portuguese) or a third country national. The paper suggests eight scenarios of individual dependence and uncertainty, which are determined by individual, organizational, and/or market factors. Such scenarios are, in turn, thought to require personal contacts with specific functions. The paper suggests eight interpersonal roles of foreign subsidiary managers, by which the functions of their personal contacts enable inter-firm coordination in industrial markets. In addition, the paper suggests eight propositions on how the functions of their personal contacts enable centralization, formalization, socialization and horizontal communication in multinational corporations.