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The environmental performance of buildings can be measured by using an existing green building indicator system. In Thailand, the Thai’s Rating of Energy and Environmental Sustainability (TREES) has been applied to 70 buildings including condominiums. It is important to collect feedback from stakeholders to identify the criteria of green features that respond to the expectation of condominium’s potential buyers as well as the satisfaction of current occupants. This paper aims to examine prioritised aspects from potential buyers and occupant satisfaction of the TREES criteria in the case study, a green condominium in Bangkok, Thailand.

The case study was conducted at IDEO Mobi Sathorn in Bangkok, the only condominium certified with the TREES system so far. Research methods include interviews, observations, document analysis and the surveys from the condominium’s potential buyers and current occupants.

The findings indicate that the condominium’s potential buyers are more concerned about site and landscape, indoor environmental quality and energy and atmosphere, whereas the current occupants are more satisfied about water conservation, site and landscape and energy and atmosphere in comparison with the other TREES criteria. Despite the provision of green features in the condominium, occupants are less satisfied about green innovation as showed in the least satisfied percentage of TREES criteria. In Facilities Management perspective, the paper shows connections between TREES criteria and FM functions in multi-unit residential project. The findings show that the application of TREES criteria focuses on the provision of value in FM, whereas stakeholder perceptions regarding the TREES criteria contribute to the perception of value in FM.

The findings and reflections upon the finding can help to understand the impact of green building aspects of the TREES system on perceptions of different stakeholders, that is, potential buyers and current occupants of the condominium. Recommendations for real estate developers and facilities managers regarding the development of green building concept on the TREES system are provided.

There has been no prior research in this area. The paper provides better understanding with regard to prioritised aspects from the potential buyers and occupant satisfaction of TREES criteria in Bangkok green condominium. This paper provides empirical data regarding stakeholder perception on TREES criteria that can be used to compare with similar data of the TREES-certified condominiums when they are available in the future.

The purpose of this study is to consider how a local government authority may present a tree asset register of street trees for the decision-making of the authority's stakeholders.

Using the tenets of population density theory, urban form theory and social stratification theory, the approach of the study is to develop a tree asset register in a local government authority's setting that could be modelled using many different attributes to derive important information for decision-making purposes.

Tree asset registers represent a critical tool in managing street trees across local government authorities.

Although the efficacy of an asset tree register may be curtailed by lack of internal audit or yearly updates, the practical consequence of an asset tree register is that local administrators may use the register to gather summarised, organised and parsimonious measures of a wide range of environmental, historical, cultural, aesthetic and scientific values of street trees.

Tree asset registers affords ratepayers, developers, tree managers and valuers a technology to plan, coordinate and manage street trees to support ecosystem services.

Asset tree registers offer planners a means to bring about sustainable change management.

The originality of the study rests in introducing tree registers as a means to meet diverse strategies for street tree management by interested stakeholders.

This paper aims to propose a methodology for managerial decision-making based on scenario planning and a multi-criteria approach.

The methodology consists of two stages, one referring to scenario planning and the other to multi-criteria decision-making. The methodology was applied to a company in the Brazilian agribusiness sector, aiming to help managers face the current situation of the COVID-19 pandemic.

The proposal addresses a set of simple methods for developing a scenario analysis based on different approaches. Although the methodology may allow the future addition of new, perhaps more robust strategies, the purpose of the analysis is not only to tell the decision maker which strategy should be adopted, but also to provide greater knowledge about the problem and possible scenarios.

The contribution of this research is to propose a structured and easily applicable methodology that can help managers in the future planning of their companies, especially when faced with complex decisions and high level of uncertainty.

Higher education institutions face ongoing accommodation problems. Considers a methodology that could assist decision makers in the identification and preliminary evaluation of various accommodation options. Identifies three key stages: specification of objectives; identification of options; and evaluation of options. For the first stage, highlights the importance of recognizing strategic objectives. In the second stage, identifies alternative ways of achieving objectives, in particular the improved use and/or change in use of space. In the third stage, considers a weighting system, that might help identify options which are most likely best to meet objectives.

This paper aims to provide a tool for decision makers to help them with selection of the appropriate supplier.

Companies often depend on their suppliers to meet customers' demands. Thus, the key to the success of these companies is selection of the appropriate supplier. A methodology is proposed to address this issue by first identifying the appropriate selection criteria and then developing a mechanism for their inclusion and measurement in the evaluation process. Such an evaluation process requires decision maker's preferences on the importance of these criteria as inputs.

Human assessments contain some degree of subjectivity that often cannot be expressed in pure numeric scales and requires linguistic expressions. To capture this subjectivity the authors have applied fuzzy logic that allows the decision makers to express their preferences/opinions in linguistic terms. Decision maker's preferences on appropriate criteria as well as his/her perception of the supplier performance with respect to these criteria are elicited. Fuzzy membership functions are used to convert these preferences expressed in linguistic terms into fuzzy numbers. Fuzzy mathematical operators are then applied to determine a fuzzy score for each supplier. These fuzzy scores are in turn translated into crisp scores to allow the ranking of the suppliers. The proposed methodology is multidisciplinary across several diverse disciplines like mathematics, psychology, and operations management.

The procedure proposed here can help companies to identify the best supplier.

The paper describes a decision model that incorporates decision maker's subjective assessments and applies fuzzy arithmetic operators to manipulate and quantify these assessments.

Evaluation of energy and climate policy interactions is a complex issue, whereas stakeholders' preferences incorporation has not been addressed systematically. The purpose of this paper is to present an integrated weighting methodology that has been developed in order to incorporate weighting preferences into an ex ante evaluation of climate and energy policy interactions.

A multi‐criteria analysis (MCA) weighting methodology which combines pair‐wise comparisons and ratio importance weighting methods has been elaborated. It initially introduces the users to the evaluation process through a warming up holistic approach for an initial rank of the criteria and then facilitates them to express their ratio relative importance in pair‐wise comparisons of criteria by providing them an interactive mean with verbal, numerical and visual representation of their preferences. Moreover, it provides a ranking consistency test where users can see the degree of (in)consistency of their preferences.

Stakeholders and experts in the energy policy field who tested the methodology stated their approval and satisfaction for the combination of both ranking and pair‐wise comparison techniques, since it allows the gradual approach to the evaluation problem. In addition, main difficulties in MCA weights elicitation processes were overcome.

The methodology is tested by a small sample of stakeholders, whereas a larger sample, a broader range of stakeholders and applications on different climate policy evaluation cases merit further research.

The novel aspect of the developed methodology consists of the combination of ranking and pair‐wise comparison techniques for the elicitation of stakeholders' preferences.

One of the main reasons for increase in mortality rate in woman is breast cancer. Accurate early detection of breast cancer seems to be the only solution for diagnosis. In the field of breast cancer research, many new computer-aided diagnosis systems have been developed to reduce the diagnostic test false positives because of the subtle appearance of breast cancer tissues. The purpose of this study is to develop the diagnosis technique for breast cancer using LCFS and TreeHiCARe classifier model.

The proposed diagnosis methodology initiates with the pre-processing procedure. Subsequently, feature extraction is performed. In feature extraction, the image features which preserve the characteristics of the breast tissues are extracted. Consequently, feature selection is performed by the proposed least-mean-square (LMS)-Cuckoo search feature selection (LCFS) algorithm. The feature selection from the vast range of the features extracted from the images is performed with the help of the optimal cut point provided by the LCS algorithm. Then, the image transaction database table is developed using the keywords of the training images and feature vectors. The transaction resembles the itemset and the association rules are generated from the transaction representation based on a priori algorithm with high conviction ratio and lift. After association rule generation, the proposed TreeHiCARe classifier model emanates in the diagnosis methodology. In TreeHICARe classifier, a new feature index is developed for the selection of a central feature for the decision tree centered on which the classification of images into normal or abnormal is performed.

The performance of the proposed method is validated over existing works using accuracy, sensitivity and specificity measures. The experimentation of proposed method on Mammographic Image Analysis Society database resulted in classification of normal and abnormal cancerous mammogram images with an accuracy of 0.8289, sensitivity of 0.9333 and specificity of 0.7273.

This paper proposes a new approach for the breast cancer diagnosis system by using mammogram images. The proposed method uses two new algorithms: LCFS and TreeHiCARe. LCFS is used to select optimal feature split points, and TreeHiCARe is the decision tree classifier model based on association rule agreements.

The purpose of this paper is to state a hierarchy methodology to select the most promising innovative projects, based on the processes defined by the successful Project Management Institute.

Open innovation is a new option for companies to acquire knowledge; however, in a changing and global market, it is necessary to define and select properly the proposals to be financially supported. A proven multicriteria decision methodology (MCDM) is recommended in this case to hierarchize alternatives. Moreover, analytical hierarchy process (AHP) has been considered in this study as a proven and simple MCDM.

AHP has been demonstrated as a suitable option to evaluate innovative project proposals, thanks to its integration with the Project Management Institute methodology. A process example has been included to demonstrate its application.

Innovative projects and project proposal selection have always implied subjective criteria. Moreover, some of the processes defined in the methodology were not well defined in the project proposal.

The management of a project portfolio in a rational way would help decision makers to fund the most promising projects/alternatives under consideration. In this way, the inherent risk of R&D projects would be minimized.

The management of a portfolio of innovative proposals is less often addressed in the literature. This paper focuses on the hybridization of the criteria and processes described in the PMBOK Guide and an MCDM.

This study aims to develop a methodology for predicting the resilience of individual firms to economic crisis, using historical government data to optimize one of the most important and costly interventions that governments undertake, the huge economic stimulus programs that governments implement for mitigating the consequences of economic crises, by making them more focused on the less resilient and more vulnerable firms to the crisis, which have the highest need for government assistance and support.

The authors are leveraging existing firm-level data for economic crisis periods from government agencies having competencies/responsibilities in the domain of economy, such as Ministries of Finance and Statistical Authorities, to construct prediction models of the resilience of individual firms to the economic crisis based on firms’ characteristics (such as human resources, technology, strategies, processes and structure), using artificial intelligence (AI) techniques from the area of machine learning (ML).

The methodology has been applied using data from the Greek Ministry of Finance and Statistical Authority about 363 firms for the Greek economic crisis period 2009–2014 and has provided a satisfactory prediction of a measure of the resilience of individual firms to an economic crisis.

The authors’ study opens up new research directions concerning the exploitation of AI/ML in government for a critical government activity/intervention of high importance that mobilizes/spends huge financial resources. The main limitation is that the abovementioned first application of the proposed methodology has been based on a rather small data set from a single national context (Greece), so it is necessary to proceed to further application of this methodology using larger data sets and different national contexts.

The proposed methodology enables government agencies responsible for the implementation of such economic stimulus programs to proceed to radical transformations of them by predicting the resilience to economic crisis of the firms applying for government assistance and then directing/focusing the scarce available financial resources to/on the ones predicted to be more vulnerable, increasing substantially the effectiveness of these programs and the economic/social value they generate.

To the best of the authors’ knowledge, this study is the first application of AI/ML in government that leverages existing data for economic crisis periods to optimize and increase the effectiveness of the largest and most important and costly economic intervention that governments repeatedly have to make: the economic stimulus programs for mitigating the consequences of economic crises.