Integrating building information to support decision-making has been a key challenge in the Architecture, Engineering, and Construction (AEC) industry. The synergy of Building Information Modelling ...(BIM) and Multi-Criteria Decision Making (MCDM) is expected to improve information integration and decision-making. The aim of this paper is to identify strategies to improve the synergy between MCDM and BIM. From the earliest literature (2009) to the present, this study examines 45 articles combining MCDM with BIM. We find that the five major application domains are sustainability, retrofit, supplier selection, safety, and constructability. Five established strategies for improving the synergy between MCDM and BIM were discussed and can be used as a benchmark for evaluating the application of decision techniques in practice. This study points out gaps of combining MCDM and BIM in the current literature. It also sheds new light into combining MCDM with BIM for practitioners, as to promote integrated decision-making.
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•Combining MCDM with BIM is useful for decision-making in construction.•The application domains and common MCDM techniques combined with BIM are categorized.•Three BIM functions in MCDM process and two synergy approaches between BIM and MCDM are reviewed.•Five strategies for the improvement of combining MCDM with BIM are provided.
Optimal land use allocation with the intention of ecosystem services provision and biodiversity conservation is one of the key challenges in agricultural management. Optimization techniques have been ...especially prevalent for solving land use problems; however, there is no guideline supporting the selection of an appropriate method. To enhance the applicability of optimization techniques for real-world case studies, this study provides an overview of optimization methods used for targeting land use decisions in agricultural areas. We explore their relative abilities for the integration of stakeholders and the identification of ecosystem service trade-offs since these are especially pertinent to land use planners. Finally, we provide recommendations for the use of the different optimization methods. For example, scalarization methods (e.g., reference point methods, tabu search) are particularly useful for a priori or interactive stakeholder integration; whereas Pareto-based approaches (e.g., evolutionary algorithms) are appropriate for trade-off analyses and a posteriori stakeholder involvement.
•We present a review of optimization techniques for land use allocation problems.•The review also considers constraint handling for the different methods.•A structured guideline for selecting appropriate optimization methods is proposed.•This guideline includes the moment of stakeholder integration and trade-off analysis.
Multi‐actor multi‐criteria analysis is a group decision‐making framework that allows multiple stakeholder groups to be involved in the decision‐making process, facilitating the understanding of the ...points of consensus and conflict among the stakeholder groups. Carefully selecting suitable criteria is important as they illustrate the possibly divergent priorities of the respective stakeholder group, and overlooking important criteria can lead to erroneous outcomes. Furthermore, the number of criteria needs specific consideration, as a too large number poses problems for human cognition, but a too small number inaccurately represents the stakeholder's interest. In stakeholder groups with many members, such as those representing citizens, defining a criteria set is likely to be even more complicated. Currently, there is no formal guideline to assist facilitators in defining these criteria sets. In this paper, we propose a novel framework for criteria preprocessing with stakeholder involvement that includes a guideline for firstly selecting criteria into a tentative list and secondly selecting the final criteria set. It provides a procedure on how to determine criteria considering the priorities of stakeholder groups with regard to the context. As a final step, we propose a mathematical model for selecting a number of criteria that are both cognitively manageable and representative for the participants' priorities. Based on the principles of the Pareto analysis, as well as the cognitive judgment theory “magic number seven plus or minus two”, a recommendation list of the criteria is generated. It prevents key criteria from being omitted while at the same time limiting the overall number of criteria. This framework is applied to a social decision‐making case for construction logistics, and the results are compared with the conventional approach of criteria definition.
•Mapping the research landscape of Fuzzy-TOPSIS into a coherent taxonomy.•Figure out the motivation of develop the Fuzzy-TOPSIS.•Highlight the open challenges that hinder the of develop the ...Fuzzy-TOPSIS.•Recommendations lists of develop the Fuzzy-TOPSIS in the literature.
A crucial topic in expert system and operations research is fuzzy multi-criteria decision making (FMCDM), which is used in different fields. Existing options and gaps in this topic must be understood to prepare valuable knowledge on FMCDM environments and assist scholars. This study maps the research landscape to provide a clear taxonomy. The authors focus on searching for articles related to (i) technique for order of preference by similarity to ideal solution (TOPSIS); (ii) development; and (iii) fuzzy sets in four primary databases, namely, IEEE Xplore, Web of Science, Elsevier ScienceDirect and Springer. These databases include literature that focuses on FMCDM. The resulting final set after the filtering process includes 170 articles, which are classified into four categories. The first, second, third and fourth categories include articles that used a type-1 fuzzy set with the TOPSIS method, a type-2 fuzzy set with the TOPSIS method, two fuzzy membership functions and a survey paper, respectively. The basic attributes of this topic include motivations for utilising FMCDM, open challenges and limitations that obstruct utilisation and recommendations to researchers for increasing the approval and application of FMCDM.
ABSTRACT
Partial least squares path modeling (PLS‐PM) has become popular in various disciplines to model structural relationships among latent variables measured by manifest variables. To fully ...benefit from the predictive capabilities of PLS‐PM, researchers must understand the efficacy of predictive metrics used. In this research, we compare the performance of standard PLS‐PM criteria and model selection criteria derived from Information Theory, in terms of selecting the best predictive model among a cohort of competing models. We use Monte Carlo simulation to study this question under various sample sizes, effect sizes, item loadings, and model setups. Specifically, we explore whether, and when, the in‐sample measures such as the model selection criteria can substitute for out‐of‐sample criteria that require a holdout sample. Such a substitution is advantageous when creating a holdout causes considerable loss of statistical and predictive power due to an overall small sample. We find that when the researcher does not have the luxury of a holdout sample, and the goal is selecting correctly specified models with low prediction error, the in‐sample model selection criteria, in particular the Bayesian Information Criterion (BIC) and Geweke–Meese Criterion (GM), are useful substitutes for out‐of‐sample criteria. When a holdout sample is available, the best performing out‐of‐sample criteria include the root mean squared error (RMSE) and mean absolute deviation (MAD). We recommend against using standard the PLS‐PM criteria (R2, Adjusted R2, and Q2), and specifically the out‐of‐sample mean absolute percentage error (MAPE) for prediction‐oriented model selection purposes. Finally, we illustrate the model selection criteria's practical utility using a well‐known corporate reputation model.
•A possibility degree formula for PLTSs rating is proposed.•A new framework is proposed to solve MCDM problems under linguistic environment.•The new comparison method can be applied to ranking HFLTSs ...as well.
The theory of probabilistic linguistic term sets (PLTSs) is very useful in dealing with the multi-criteria decision making (MCDM) problems in which there is hesitancy in providing linguistic assessments; and PLTSs allow experts to express their preferences on one linguistic term over another. The existing approaches associated with PLTSs are limited or highly complex in real applications. Hence, the main purpose of this paper is to establish more appropriate comparison method and develop a more efficient way to handle with MCDM problems. We first put forward a diagram method to analyze the structures of PLTSs and develop the visualized way for readers to comprehend. Then a possibility degree formula is given for ranking PLTSs. Based on the new comparison method and the theory of the fuzzy preference relation, an efficient decision-making framework is proposed to solve real-life problems under linguistic environment. Conventional TOPSIS methods combined with PLTSs are also included for comparison. All results demonstrate the practicality of the new framework. Finally, we also seek out relationship between PLTSs and hesitant fuzzy linguistic term sets (HFLTSs), and compare the new formula with the similar approaches to HFLTSs’ rating.
•We investigate MCDA method selection problems for a particular decision situation.•We provide a generalized framework for selecting MCDA methods for decision-making situations.•We provide a set of ...rules based on a comprehensive set of MCDA methods.•We address the lack of knowledge issue in the decision-making situation description.•We provide a web based expert system facilitating the selection of a MCDA method in cases of both complete and partial knowledge about the decision-making situation.
Multi-Criteria Decision Analysis (MCDA) methods are widely used in various fields and disciplines. While most of the research has been focused on the development and improvement of new MCDA methods, relatively limited attention has been paid to their appropriate selection for the given decision problem. Their improper application decreases the quality of recommendations, as different MCDA methods deliver inconsistent results. The current paper presents a methodological and practical framework for selecting suitable MCDA methods for a particular decision situation. A set of 56 available MCDA methods was analysed and, based on that, a hierarchical set of methods' characteristics and the rule base were obtained. This analysis, rules and modelling of the uncertainty in the decision problem description allowed to build a framework supporting the selection of a MCDA method for a given decision-making situation. The practical studies indicate consistency between the methods recommended with the proposed approach and those used by the experts in reference cases. The results of the research also showed that the proposed approach can be used as a general framework for selecting an appropriate MCDA method for a given area of decision support, even in cases of data gaps in the decision-making problem description. The proposed framework was implemented within a web platform available for public use at www.mcda.it.
A hierarchical decomposition is a common approach for coping with complex decision problems involving multiple dimensions. Recently, the multiple criteria hierarchy process (MCHP) has been introduced ...as a new general framework for dealing with multiple criteria decision aiding in case of a hierarchical structure of the family of evaluation criteria. This study applies the MCHP framework to multiple criteria sorting problems and extends existing disaggregation and robust ordinal regression techniques that induce decision models from data. The new methodology allows the handling of preference information and the formulation of recommendations at the comprehensive level, as well as at all intermediate levels of the hierarchy of criteria. A case study on bank performance rating is used to illustrate the proposed methodology.
•A bibliometric based survey on AHP and TOPSIS methods has been conducted.•Scopus database was employed to retrieve the required data for this analysis.•Assessment of quantitative and qualitative ...bibliometric indicators was obtainable.•Efficacy of these methods promotes the development of related research.•More scientific research interests will be devoted to these methods in the future.
In recent years, the employment of multiple criteria decision analysis (MCDA) techniques in solving complex real-world problems has increased exponentially. The willingness to build advanced decision models, with higher capabilities to support decision making in a wide range of applications, promotes the integration of MCDA techniques with efficient systems such as intelligence and expert systems, geographic information systems, etc. Amongst the most applied MCDA techniques are Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The development of a comprehensive perspective on research activities associated with the applications of these methods provides insights into the contributions of countries, institutes, authors and journals towards the advancements of these methods. Furthermore, it helps in identifying the status and trends of research. This in turn will help researchers in shaping up and improving future research activities and investments. To meet these aims, a bibliometric analysis based on data harvested from Scopus database was carried out to identify a set of bibliometric performance indicators (i.e. quantitative indicators such as productivity, and qualitative indicators such as citations and Hirsch index (h-index)). Additionally, bibliometric visualization maps were employed to identify the hot spots of research. The total research output was 10,188 documents for AHP and 2412 documents for TOPSIS. China took a leading position in AHP research (3513 documents; 34.5%). It was also the leading country in TOPSIS research (846 documents; 35.1%). The most collaborated country in AHP research was the United States, while in case of TOPSIS it was China. The United States had gained the highest h-index (78) in AHP research, while in TOPSIS it was Taiwan with h-index of 46. Expert Systems with Applications journal was the most productive journal in AHP (204; 2.0%) and TOPSIS research (125; 5.2%), simultaneously. University of Tehran, Iran and Islamic Azad University, Iran were the most productive institutions in AHP (173; 1.7%) and TOPSIS (115; 4.8%) research, simultaneously. The major hot topics that utilized AHP and will continue to be active include different applications of geographic information systems, risk modeling and supply chain management. While for TOPSIS, they are supply chain management and sustainability research. Overall, this analysis has shown increasing recognition of powerful of MCDA techniques to support strategic decisions. The efficacy of these methods in the previous context promotes their progress and advancements.
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